AI Automation Engineer in the US

Quick answer: what is an AI Automation Engineer?

The 4-stage roadmap at a glance

The four stages on this roadmap total a suggested planning range of 13 to 25 weeks of part-time study, about three to six months. For a working engineer, a separate suggested range of three to six months covers reaching a hireable portfolio. The two measure different things: the stage sum is study and practice time, and the background range adds portfolio work. For engineers they happen to land in the same window. The stages run in order, and each ends with a working workflow.

Assumptions: you can already script in Python and call an API, you study about 5 to 10 hours a week alongside full-time work, and each range includes building the stage's practice workflow but not separate portfolio polishing or a job search. Every range is reasoned from official course lengths, not a measured outcome. Each stage is expanded with what to have done before you start, what you learn, what you build and how you know you’re done in the full roadmap, where the evidence behind each estimate is set out.

What is an AI Automation Engineer?

An AI Automation Engineer designs, builds and maintains business workflows that run automatically end to end, using large language models, APIs and integration platforms inside a path the engineer defines. The defining shift from classic automation is that some workflow steps are performed by a model that reads unstructured input, such as an email, an invoice or a support ticket, and returns structured output the next step can act on.

The engineer's deliverable is a running workflow: a trigger, a defined sequence of steps, the model calls inside it, connectors to the business systems it reads and writes, error handling, a review step where accuracy matters, and a measure of the time or cost it saves. The role uses existing models through their APIs rather than training them, and the path the workflow follows is designed by the engineer rather than decided by the model at runtime. The distinction from neighboring AI roles is set out under AI Automation Engineer vs other AI roles.

Is AI automation engineering a real job?

Yes. AI Automation Engineer appears in US job-board categories and listings, alongside the closely related titles AI Automation Specialist and AI Workflow Automation Specialist, and AI Automation Developer is used interchangeably with AI Automation Engineer in postings. The title is a specialization within the broader Automation Engineer title, and employers use both titles differently, so much of the work is also advertised under the broader title. What the job-market evidence can and cannot show is set out under jobs.

Which kind of automation engineer is this page about?

The Automation Engineer title is used for at least five different kinds of work, and search results for the bare title mix all of them. This page covers the AI and workflow automation track. The other tracks appear only where they are a starting point for moving into AI automation.

Grouping from the salary and placement research behind this page, which found the Automation Engineer title used for RPA and business-process, DevOps and infrastructure, and industrial automation, with test automation as a distinct discipline and AI automation as the AI-augmented specialization. Salary sites report these kinds of work under one title, which is why the pay figures on this page are labeled with the exact title each one measures.

What an AI Automation Engineer does day to day

The work divides into seven recurring responsibilities. The last one, measuring what the automation saves, is what shows an automation is worth keeping.

An editorial description built from Simplilearn's AI Career Pathways skill progression for this role. It is not a count of how often postings list each responsibility, and it does not say how the working week divides between them.

How is the value of an automation measured?

An automation is judged by what it changes in the process it replaced. Five measures cover the main dimensions of value, and each needs a baseline recorded before launch, because a saving cannot be shown against a process nobody timed.

Measurement framework written for this page. It describes what to record, not a benchmark, and quotes no typical savings or error rates.

Owning a business-critical automation with measured time or cost savings is also the seniority signal for this role, as set out under career progression.

AI Automation Engineer salary: what the US evidence shows

Reliable salary evidence for the exact title AI Automation Engineer in the United States is limited. Glassdoor's page for the title showed only 7 salary submissions when it was captured in September 2026, and ZipRecruiter's page for the title returns the same figures as its generic Automation Engineer page, so neither sets a figure here. A linked sample of US employer postings with advertised pay has not yet been collected for the title, so this guide publishes no AI Automation Engineer salary figure.

Broad-title context, not an AI automation salary. Generic Automation Engineer figures include controls, industrial, QA, DevOps and RPA roles as well as AI workflow work, so they should not set your salary expectation for an AI-workflow role. For that broad title, Indeed reports an average base salary of $108,324 a year and, separately, a $6,000 average cash bonus, from 5.2k salaries in job postings over the past 36 months (updated October 5, 2026; read October 8, 2026). Glassdoor reports average total pay of $118,843 from 6,600 employee-submitted salaries (last updated April 10, 2025, as captured in September 2026), and Payscale an average base salary of $86,556 from 1,225 profiles whose skill list leans toward industrial automation (last updated April 2, 2026). The three measure different things, posted base pay, self-reported total pay and profile base pay, and are not averaged. As official context, the Bureau of Labor Statistics reports a 2025 median annual wage of $134,040 for software developers, quality assurance analysts and testers combined, an occupation group that is broader than this role.

How to judge the pay for a specific AI automation role

Read the range the posting itself advertises, and check whether it is base pay alone or a total that adds bonus or equity. For one job, that posting's own range is better evidence than any title average. Compare offers on the same basis, base with base, and treat a range that spans junior and senior work as a range, not a typical figure.

AI Automation Engineer jobs and hiring demand

Evidence limitation. This guide has no dated, linkable count of US postings for the exact title, no structured sample of posting requirements and no systematic list of hiring employers. An attempt on October 7, 2026 to build a linked sample from employer job boards found that most search-indexed postings had closed, and too few live US postings could be verified to analyze. The skills, prerequisites and entry guidance on this page are therefore editorial guidance built from Simplilearn's skill progression, not measured employer demand.

What official labor statistics cover

AI Automation Engineer has no occupation code in US government statistics. As broader labor-market context only, the US Bureau of Labor Statistics projects 10% employment growth from 2025 to 2035 for software developers, quality assurance analysts and testers collectively, an increase of 185,400 jobs, and about 106,100 openings a year, a figure that includes replacing workers who change occupations or leave the labor force. It is not a forecast for AI automation engineering.

Job titles this work is advertised under

Titles observed in US job-board categories and listings in the research behind this page, September 2026. They are listed as observed usage, not ranked or counted. Searching only for the exact title AI Automation Engineer will miss work advertised under the broader titles.

AI Automation Engineer career progression and future scope

The levels below are an editorial guide to how scope and evidence grow with seniority. Employers name and define levels differently, so read each posting's own description of scope.

Editorial guidance, not published promotion criteria. The seniority signal used here, owning a business-critical automation with measured time or cost savings, is Simplilearn's summary of the role.

What the senior and lead stage involves

At senior level the work often changes from building workflows to owning them: choosing platforms, setting the standards for error handling, review steps and monitoring that other engineers follow, justifying automation work with measured savings, and mentoring. Natural next steps include an automation architect or practice lead role, or independent consulting. This guide suggests building toward them with a record of automations that kept running and paid for themselves.

Employment or freelance work

Freelance and consulting work is a parallel route, and it changes the shape of the job: you find and scope your own clients, price your own work and cover a wider range of systems. This guide quotes no freelance rates. Independent work is more realistic once you have a portfolio, at least one automation delivered for a real stakeholder, and the ability to scope and price a project before you build it.

Future scope: is AI replacing automation engineers?

No official forecast exists for this role; the broader labor-market context is set out under jobs. This guide's editorial outlook is that AI changes the unit of work: less scripting of individual tasks, more design, testing and governance of workflows that contain model steps. A rule-based invoice bot becomes a workflow that reads invoices in varied layouts and routes the uncertain ones to a person; a scripted report becomes a workflow that also drafts the summary. The skill that is likely to hold its value is deciding where a model belongs in a process and proving the result.

AI Automation Engineer vs other AI roles

Three AI roles are easy to confuse, because all three put language models into working software. Their skills overlap and employers use the titles differently; the clearest difference is in what each one typically delivers.

An AI Automation Engineer builds a workflow that runs a path you defined. An Agentic AI Engineer builds a system that decides its own path.

An AI Engineer ships a feature that calls a model. An AI Automation Engineer ships a workflow that uses models to run a business process end to end.

Simplilearn's editorial summary of each role's typical emphasis; the same rows appear on each of these role guides. Production ownership and tooling vary by team. There is no salary column: each role's pay evidence comes from a different sample on a different date, so this guide does not compare pay across roles.

The overlap with agentic engineering is orchestration, and the line is control flow. An automation starts from a defined business workflow and follows a path the engineer authored, so it is measured by ROI, time saved and tasks automated, and its typical failure is a broken integration. An agentic system starts from an open-ended goal and decides its steps at runtime, so it is measured by task success under uncertainty and cost per run, and its typical failure is an agent that loops or takes a wrong action. Multi-agent orchestration, agent memory and agent evaluation are covered in depth on the Agentic AI Engineer career roadmap.

This guide focuses on workflow automation and integration. See the Machine Learning Engineer career roadmap for model training and tuning, and the AI Engineer career roadmap for retrieval-augmented generation and prompt engineering as application patterns. An AI Automation Engineer often uses all three as inputs to a workflow rather than as the main work.

How AI automation differs from rule-based automation

AI automation keeps the process discipline of rule-based automation and adds steps that a model performs. What changes most is how failure has to be caught: a rule-based bot fails loudly, while a model step can fail quietly by returning a confident wrong answer.

Comparison written for this page from the skill progression and the rule-based and AI automation tools named in its research. It describes the work, not hiring outcomes.

How the title AI Automation Engineer is used

AI Automation Engineer is an AI-augmented specialization within the broader Automation Engineer title. Job-board categories also use AI Automation Specialist and AI Workflow Automation Specialist for similar work.

Is AI automation the same as RPA?

Not quite; they overlap. RPA automates rule-based tasks, often by operating an application's user interface, and remains the right tool for stable, structured, high-volume work. AI automation adds model steps for input that rules cannot handle reliably, and an AI workflow can still contain RPA or rule-based steps. RPA experience is an accelerant for this role rather than a prerequisite: process mapping, exception handling and stakeholder work transfer directly.

Skills required for an AI Automation Engineer, and skill levels

An AI Automation Engineer needs four groups of skill: workflow design, system integration, LLM API orchestration and ROI measurement. Simplilearn's skill progression places the role's primary skills at L4 and L5, workflow automation and enterprise integration, on top of a shared foundation in prompt engineering and tool use.

The skill progression, L1 to L5

Levels, titles and skills are verbatim from Simplilearn's AI Career Pathways framework. Which skills this guide emphasizes is an editorial choice within a track shared with the Agentic AI Engineer role, and the skills overlap. The framework marks L4 and L5 as the expert levels its programs are designed to reach. The skill levels, depth recommendations and artifacts below are an editorial preparation framework, not counts from job postings.

Skill levels: beginner to expert

The four proficiency levels used across this page map onto the progression above.

What to build at each level

How deep each skill needs to go

Depth levels are this guide's recommendation. Foundational means you can follow existing patterns, Applied means you can build independently, and Expert means you can set standards others follow.

How these skills are named in credentials

Program pages use a consistent vocabulary for these skills. The names below are verbatim from the four mapped programs' skill lists, mapped to the plain-language skill they describe.

Which workflow platform to learn first

Learn n8n first. It is the workflow tool named in this role's skill progression, and three of the four programs mapped to the role list it among their tools. That is a reason to start with it, not evidence that employers name it most often; curriculum coverage is not a measure of employer demand. Triggers, branching, error paths and retries work the same way across platforms, so the first choice is low-risk and the second platform takes a fraction of the time.

Platform descriptions are general. This page does not compare platform features or pricing, which change frequently and belong to the vendors.

APIs, webhooks and system integration

Integration is the half of the job that no workflow platform removes. Every automation reads from and writes to other systems, and a failure at one of those boundaries is one that no model step can fix.

How much LangChain, MCP or A2A you need

Usually enough to use them, not to specialize in them. LangChain and LangGraph are covered in more depth in the Agentic AI Engineer guide; for most automation work you need enough to read a chain and call it from a workflow. MCP and A2A are on this role's L4 skill list as open protocols for exposing tools to models and connecting agents, so learn to consume and publish a simple MCP tool. Building multi-agent systems and running agent evaluation in depth are covered on the Agentic AI Engineer career roadmap.

Which programming languages matter

Python is the language to learn first. It appears in the salary-site skill lists for the Automation Engineer title, it is the default for custom workflow steps and model SDKs, and it covers data transformation. SQL is the second, because many automations read from or write to a database. Industrial automation uses ladder logic and structured text instead, which is one sign that it is a different track.

The AI automation stack in 2026

The AI automation stack has eight layers. An AI automation touches at least four of them: a trigger in a workflow platform, a model call, an integration with a business system, and logging for monitoring.

Tools are named from the skill progression, Simplilearn's role comparison, the salary research and the tool lists of the four mapped programs. Tools appear as text so every fact is readable without images.

AI automation roadmap: how to become an AI Automation Engineer

The roadmap has four stages, taken in order from Simplilearn's skill progression for this role, and each ends with a working workflow you can show. Together they total a suggested planning range of 13 to 25 weeks, about three to six months, assuming you can already script in Python and call an API and study about 5 to 10 hours a week, with each stage's practice build included.

Stage by stage

L2 of the progression, context engineering and retrieval-augmented generation, is not a stage on this roadmap; it is covered in more depth in the Agentic AI Engineer guide. If you want enough retrieval awareness to add a knowledge-base step to a workflow, plan an optional 1 to 2 part-time weeks, which is excluded from the totals above. Every duration is a suggested planning range, not a measured outcome. The before-you-start and done checks are editorial guidance built from the stage order and each stage's build, not published benchmarks.

The evidence behind each duration estimate

Each estimate is built from the official length of courses that teach closely matching content, converted to a part-time pace. A course length is adjacent evidence: it does not measure how long it takes to reach competence in the role.

Every source was fetched on September 23, 2026, and each stated figure is what the provider's own page says. Tool-introduction courses are short; most of each stage's time goes into building and debugging workflows against real systems. Stage 4 carries the widest range because the enterprise platform you choose changes the load.

How long it takes from your starting background

The stage sum covers study and practice time. The ranges below are suggested planning ranges to a hireable portfolio, including portfolio work and, for career switchers, the job search, so they can legitimately exceed the stage sum. They are conditional on the same 5 to 10 hours a week, and where no transition data was found for a background, no month range is published.

Solution architect and techno-functional ranges rest on editorial and vendor sources only. The no-technical-background range includes a scripting and API foundation that is not a stage in the skill progression, and a first role may be a junior automation or integration title. Simplilearn program lengths are course lengths, not time to job-ready, and bootcamp outcomes for broad software careers are adjacent evidence for this role at best.

The three stages between learning and hiring

A framework written for this page. Employers see only the third stage; the first two are how you reach it.

Prerequisites and qualifications for an AI Automation Engineer

You need scripting, an understanding of APIs and JSON, and one workflow platform before the roadmap's later stages. Business process knowledge matters more than it first appears, because one of the hardest decisions in an automation is which steps to automate at all.

Do you need a degree to become an AI automation engineer?

This guide states no degree requirement either way, because degree requirements in AI automation postings have not been measured. One industry pay guide, from Layer3 Labs, states that platform certifications plus a small portfolio carry more weight than a degree for entry-level RPA and AI automation roles; that is one publisher's view, not a measured finding. Degree-based timelines of four to six years, such as the one BestColleges gives, describe industrial automation engineering and are not comparable with this role.

Do you need to know how to code?

Yes, at least scripting. Simplilearn's career framework routes learners toward this track only if they code regularly or understand code; it has no path into the role for people who do not code at all. The coding floor rises with the complexity of the systems you connect, not with the job title.

Can you get hired with no professional experience in the field?

This guide makes no claim about how often candidates without automation experience are hired. What you can control is the evidence: documented automations, at least one delivered for a real stakeholder even if unpaid, and before-and-after measurements. All four programs mapped to this role prefer at least 2 years of professional experience, and none makes it mandatory.

Career-switch paths into AI automation by background

Moving in from an existing role is common for AI roles generally. In the UK government's AI Labour Market Survey 2025, 74% of organizations had hired into AI roles from a previous role that did not involve AI, and 88% relied on on-the-job training rather than structured programs for AI skills. Those figures are for the UK and for AI roles generally, not for this role in the United States.

Timelines are suggested planning ranges from the roadmap research and carry its evidence-strength labels; where no transition data was found, no range is given. What transfers and what to add are this guide's editorial guidance.

From QA or test automation

Test automation engineers already script, think in test cases and read failures for their cause, which covers much of what testing a model step requires. The gap is on the other side: designing the model steps themselves and integrating with business systems rather than applications under test. A good crossover project is an automation whose model step is covered by a test set you would accept as a QA engineer.

From RPA development

RPA developers bring process mapping, exception handling and stakeholder work, which transfer directly. What changes is that a model step can be wrong without failing, so testing moves from fixed expected outputs to test sets and sampled review, and integration moves from the user interface to APIs where they exist. Convert one existing rule-based bot into an AI-assisted workflow and document what changed.

With no technical background: the first 90 days

Spend the first 90 days on a foundation, not on the role: Python basics, then calling a REST API and reading JSON, then one simple automation in a visual platform such as Zapier or Make. That is a starting sequence, not a timeline to employment; the suggested planning range to a hireable portfolio is nine to fifteen months part-time, including the job search.

AI automation portfolio projects that show you can do the job

Build two or three projects deeply rather than ten shallowly. Each should run against real or realistic systems, fail in documented ways and show what it saves.

Projects map to the stages of the roadmap on this page. They are suggestions, not a list any employer has published.

Suggested portfolio project: invoice intake and approval

One worked example shows what depth looks like. It is an editorial project suggestion, not a measured result or an employer requirement, and it reports no accuracy or savings figures until you have run it.

• Workflow. Receive an invoice, extract its fields with a model step, validate them against business records, route exceptions to a person, request approval, and write the approved result to the target system.
• Inputs. A small test collection of invoices you are permitted to use, or synthetic ones labeled as synthetic, deliberately including missing fields and duplicates.
• Deliverables. The workflow definition, the validation rules, a failure log, the approval history and a written recovery procedure.
• Failure cases to design for. Duplicate webhook delivery, a provider timeout, malformed model output, a missing supplier record, and an unauthorized payment instruction hidden in the invoice text.
• Evidence to show. Idempotency tests, retry behavior, correct escalation of exceptions, least-privilege integration credentials and the cost of each test run.
• Critical boundary. A model's parsed output is not payment authorization. Approval and permission checks sit outside the model, in rules and human sign-off.

What production-grade means for AI automation

Production-grade means someone can depend on the automation. It is the hardest part to self-teach, because the failures only appear with real volume, real systems and real users, and it is the part a demo never shows.

What happens when an automation fails in production?

Three failure types are worth planning for. An upstream API changes and an integration breaks: the fix is monitoring that alerts on the first failure, a retry and dead-letter queue so nothing is lost, and a runbook. A model step returns a confident wrong output: the fix is validation against a schema, a confidence rule that routes uncertain cases to review, and an audit trail that shows which runs were affected. A volume spike hits rate limits and cost: the fix is backoff, cost ceilings and alerts on spend.

Ownership varies by team, but typically the engineer owns the runbook and the fix, while the business owner decides the review threshold and signs off the measured result, because they carry the consequence of a wrong output. Guardrails and governance at organization scale are covered on the Agentic AI Engineer career roadmap.

AI Automation Engineer interview preparation

Prepare by competency area rather than by question list. Each area below maps to a stage of the roadmap, and your portfolio is the best evidence for all of them.

Competency areas are derived from the skill progression for this role. This is an editorial preparation framework, not a description of how any employer interviews.

Do you need a certification to become an AI Automation Engineer?

No certification is required for the role, and none substitutes for automations you can demonstrate. What a credential does well is give you structure and a verifiable signal when you have no production work to point at yet, so it is likely to matter more for career switchers than for engineers who already ship.

Treat this ordering as editorial reasoning rather than measurement: an automation you can demonstrate, evidence of how it fails and what you did about it, a measured result, and then credentials.

If you want a structured path, see AI automation courses and certifications for program details, curriculum and enrollment.

Is an online specialization enough to get hired?

No single course or specialization is enough on its own, regardless of provider. Coursework reliably establishes tool fluency and a first working build. It does not by itself produce a portfolio with measured results, integration against real business systems, production monitoring practice or interview readiness. Self-directed learning can work for motivated people who already have a technical base. The integration and production stages are where feedback on your work matters most, and that feedback is the hardest part to arrange alone.

Vendor certification or program certificate?

The two attest to different things. Vendor credentials and learning paths, such as UiPath Academy's Automation Developer Associate training, n8n's beginner certification course or Microsoft's expert-level Agentic AI Business Solutions Architect certification, attest to competence inside one vendor's platform. A program certificate attests to completing a structured curriculum; the four programs mapped to this role include industry projects. This guide makes no claim about which type employers prefer, and quotes no salary premium for any automation certification.

This page makes no claim about placement rates, salary increases or job guarantees. No placement rate is published for any Simplilearn program in this space.

The biggest mistakes people make learning AI automation

Key terms explained

AI automation

AI automation is the use of language-model steps inside an automated business workflow, so the workflow can read and act on unstructured input such as emails, documents or tickets alongside its rule-based steps.

Workflow automation

Workflow automation is the design of a sequence of steps that runs without manual handling once a trigger fires, moving data between systems and applying rules, checks and approvals along a defined path.

Robotic process automation (RPA)

Robotic process automation is rule-based software that performs repetitive tasks in business applications, often by operating the application's user interface the way a person would, using tools such as UiPath, Automation Anywhere or Power Automate.

n8n

n8n is a workflow automation platform in which a workflow is built from connected nodes for triggers, app connectors, logic, custom code and model calls. It is the workflow tool named in this role's skill progression.

Trigger

A trigger is the event that starts an automated workflow, such as a new email, a form submission, a schedule or an incoming webhook.

Webhook

A webhook is an HTTP request that one system sends to another when an event happens, so the receiving workflow can start immediately instead of repeatedly checking for changes.

REST API

A REST API is an interface that exposes a system's data and actions over HTTP, with requests and responses usually formatted as JSON. Many workflow integrations are built on one.

OAuth

OAuth is an authorization standard that lets a workflow act on a user's or organization's behalf in another system through a scoped, revocable token instead of a stored password.

Function calling and tool use

Function calling is the mechanism by which a language model requests execution of a defined external function with structured arguments, letting a workflow step act on systems outside the model.

Structured output

Structured output is model output constrained to a defined schema, such as JSON with named fields, so the next workflow step can use it without manual parsing.

Model Context Protocol (MCP)

Model Context Protocol is an open standard for exposing tools, data sources and prompts to AI models through a consistent interface, so an integration built once can be used by any compatible client.

Agent2Agent protocol (A2A)

The Agent2Agent protocol is an open protocol for AI agents to discover one another's capabilities and exchange tasks and results across different frameworks and vendors.

Human-in-the-loop

Human-in-the-loop is a design pattern in which defined workflow outputs pause for a person's review or approval before the workflow acts on them, used where accuracy matters or an action is hard to reverse.

Idempotency

Idempotency is the property that running the same step twice has the same effect as running it once, which lets a failed workflow step be retried without creating duplicate records or payments.

Enterprise integration

Enterprise integration is the work of connecting an organization's core systems, such as ERP, CRM, HR and finance applications, so data and actions flow between them reliably and securely.

Automation ROI

Automation ROI is the measured value of an automation against its cost, usually expressed through handling time removed, error rates, exception rates and cost per run compared with the process before automation.

AI Automation Engineer: frequently asked questions

How we compiled this data

Compensation figures on this page come from named, dated United States sources and are labeled with what each one measures: base salary or total pay, self-reported or derived from job postings. A separately reported cash bonus is never counted as base pay, and sources that measure different things are reported side by side rather than averaged.

What was collected, and how

How this page was produced

This page was researched and drafted with the help of AI tools. Every figure on it traces to a named source recorded in the page's research files, and each source is listed with the date it was checked in the table above. Where the evidence was missing or could not be read, the page says so rather than estimating. The reviewers named on this page checked it after it was built, including its title, description, image descriptions and structured data as well as the body text.

Limitations

Exact-title salary evidence is too thin to publish (Glassdoor showed 7 submissions; ZipRecruiter repeats its generic figures), so the broad Automation Engineer title, which mixes AI, RPA, test, DevOps and industrial work, appears only as labeled context. AI Automation Engineer has no occupation code, so the BLS figures describe a broader combined occupation, not this role. There is no dated, linkable posting count, no structured posting-requirement sample and no systematic employer list: an October 7, 2026 attempt to build a linked sample of live US postings found too few to analyze, so skills, prerequisites and entry guidance are editorial guidance from Simplilearn's skill progression. Timelines are suggested planning ranges built from course lengths and adjacent transition data, with no range published for QA, RPA or controls engineers. Salary figures from the September 2026 research carry each source's data date rather than an access date, and the page makes no cross-role salary comparison.

Reviewers

This page was reviewed by four Simplilearn Generative AI and AI and Machine Learning authors and industry experts.

Simplilearn authors and industry experts who reviewed this page

Reviewer	Role	Relevant expertise	Profiles
Dr. Darshan Ingle	Agentic AI & Model Chaining Expert	Agentic AI and LangChain; writes on Claude for workflow automation and AI agent memory	Simplilearn profile LinkedIn
Akshay Badkar	AI Specialist and GenAI Mentor	AI automation, workflow tools and AI agents; author of a 2026 roadmap for building an AI agent from scratch	Simplilearn profile LinkedIn
Sayan Dey	Generative AI & MLOps Specialist	Deploys generative AI systems with LLMs, retrieval and agents; covers the foundations and production stages	Simplilearn profile LinkedIn
Keith R. Worfolk	Generative Agentic AI/ML Expert	25+ years as developer, architect and CTO across cloud and enterprise platforms; covers enterprise integration	Simplilearn profile LinkedIn

Full profiles at Simplilearn Authors and Industry Experts.

About the author

Jitendra Kumar is the Chief Technology Officer at Simplilearn, leading enterprise AI readiness and generative AI strategy. An IIT Kanpur alumnus and technology entrepreneur, he specializes in building complex AI systems integrated with scalable edtech solutions that drive personalization, platform performance, workforce readiness and learner outcomes.

Full profile and published articles: Jitendra Kumar on Simplilearn · LinkedIn