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AI automation engineer job description template

A job description for a fresher AI automation engineer, with an intern version, screening questions and the points where a person must approve what a workflow does.

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In short

A fresher AI automation engineer builds workflows that connect business tools and cut repetitive typing, using n8n, Zapier, Make or Power Automate, webhooks, APIs and language-model steps that sort emails, pull fields from documents or draft replies. A person approves steps where a mistake would matter, such as refunds and customer messages. Ask for API and JSON basics, careful testing and one workflow the candidate can explain.

AI automation engineer job description template

Fill in your company details once and both versions below update: the full-time job for freshers and the internship. Copy the one you need into your job post.

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1. Full-time AI automation engineer (fresher)

AI automation engineer (fresher)

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Full-time
CTC: [CTC for the full-time role]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

Some work at [Company name] still means copying details between tools, sorting a shared inbox or typing the same reply again. The [Team] team is hiring a fresher AI automation engineer to build workflows that take over those steps. You will map each task with its owner, build and test the workflow, add a language-model step where it helps and keep a person in the loop where a mistake would matter. A senior reviews each workflow before it runs on live data.

What you will do

  • Map the task with its owner before building: record the trigger, steps, data, exceptions and named approvers, then ask the owner to confirm the map
  • Build and test workflows using [n8n / Zapier / Make / Power Automate / Python / JavaScript], with a senior’s review before anything runs on live data
  • Connect tools through webhooks and REST APIs: read the other tool’s documentation, test each call in Postman or curl and map the fields from one system to the other
  • Add language-model (LLM) steps for jobs such as sorting emails into categories, pulling fields out of documents and drafting replies, with a clear prompt, an example of the expected output and a fixed output format such as JSON
  • Place a human approval step wherever a mistake would matter, such as refunds, messages to customers and anything with personal data, so the workflow sends a draft to a named person instead of acting alone
  • Handle failures: stop the same item being processed twice, retry a failed call a limited number of times, and send items that still fail to a list or alert that someone reads
  • Test each workflow on sample data with blanks, repeated items, odd formats, a case the model gets wrong and an email or document that contains instructions aimed at the model, and record the results before go-live
  • Record each run’s time, item ID, outcome and approval status. Log model output only where the team’s data policy permits, with sensitive details removed. Use a company model key, apply available spending controls and review accuracy and cost with your senior each week
  • Store credentials in the team’s approved store and keep them out of source control, shared sheets and screenshots; send customer data only to tools the company has approved
  • Write a one-page run note for each workflow: what it does, who owns it, what it connects to, how to pause it and what to check when it fails

What we are looking for

  • REST API and JSON basics: you can read API documentation, make a request in Postman or curl, and explain a status code, a header and an API key
  • Where a workflow needs a code step, enough Python or JavaScript to reshape JSON, call an API and handle an error
  • A plain understanding that a language model can return a confident wrong answer, and the habit of checking its output against the source
  • A habit of testing with awkward cases: blank fields, repeated items, wrong formats and a failed call
  • Clear writing, to document a workflow and to explain it to the person who owns the process
  • One workflow or script that connects two tools, built with n8n, Zapier, Make, Power Automate or code during coursework, an internship or a personal project, and that you can walk us through or describe if it uses private data

Good to have

  • Basic SQL for reading from or writing to a database table
  • Google Apps Script or Office Scripts for automation inside spreadsheets
  • OAuth basics: what an access token is and why it expires
  • Prompt writing with examples, and a fixed set of test inputs to rerun after each change
  • Familiarity with one business process, such as invoice handling, lead follow-up, onboarding or ticket sorting

Who can apply

Final-year students and graduates of any stream can apply, including B.E./B.Tech, BCA, B.Sc, MCA, M.Sc, BCom, BBA and MBA. One workflow or script that the candidate can explain matters more than the stream.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

2. AI automation engineer internship

AI automation engineer intern

Company: [Company name]
Location: [Location] · Work mode: [Work mode]
Employment type: Internship · Duration: [Internship duration]
Stipend: [Stipend for the internship]
Joining: [Joining date] · Apply by: [Apply by]

About [Company name]

[About your company]

About the role

[Company name] is looking for an AI automation intern to build one small workflow with a mentor. You will map a repeat task, build and test the workflow on sample data, add a language-model step with a person checking its output, and write a run note. Any publication or portfolio use of your work needs approval from [Company name].

What you will do

  • Map one repeat task with its owner, such as sorting a shared inbox of enquiries or copying form entries into a sheet, and draw the steps on one page
  • Build a workflow that starts from a form, an email or a webhook and writes its result to a sheet or a database table
  • Add one language-model step, such as labelling each message as an enquiry, a complaint or a request, using a company model key rather than a personal one, and test it on made-up messages whose correct labels you write in advance
  • Test the workflow with repeated, blank and badly formatted inputs, then list what broke and how you fixed it
  • Fill in a run log and write a half-page run note so the next person can pause, restart and check the workflow
  • Demonstrate the workflow to its owner and list the cases it sends to a person

What we are looking for

  • A student or recent graduate from any stream, such as computer science, IT, commerce or management
  • Has connected two apps with an automation tool or a short script, even from a course
  • Can explain an API, JSON and a webhook in plain words
  • Can say why a model’s answer needs a human check before it reaches a customer
  • Can suggest test cases for blank inputs, repeated items and a failed API call, and record the expected and actual results.

Who can apply

Final-year students and graduates of any stream can apply, including B.E./B.Tech, BCA, B.Sc, MCA, M.Sc, BCom, BBA and MBA. One workflow or script that the candidate can explain matters more than the stream.

How to apply

Apply through this job post by [Apply by]. Shortlisted candidates will hear from us about the next steps.

Other titles for this role

Junior AI automation engineer, AI automation specialist (fresher), Workflow automation engineer (fresher), Automation engineer (AI and low-code).

Screening questions to add to your job post

  1. A workflow sometimes sends the same email twice. What do you check? Look for duplicate triggers, overlapping runs or a retry after a timeout. Track each item by a stable ID and check whether the email was already sent before retrying. If the outcome is unclear, stop and ask a senior rather than sending again. Accept plain words; naming a particular tool or technical pattern is not required.
  2. Which steps of an automated refund flow need a person to approve? Look for the payment itself, amounts above a limit, unclear cases and any message that promises something to the customer. Accept approval of every refund at first, relaxed later with the owner’s agreement.
  3. What is a webhook, in one or two lines? Look for one system sending a message to a web address as soon as something happens, instead of another system asking again and again. Accept an everyday comparison.
  4. How would you check that a language-model step pulls the right fields from invoices or emails? Look for a small set of sample documents with the correct values written down, a field-by-field comparison and a rerun after any prompt change. Accept a spreadsheet as the record.
  5. Where would you keep an API key for a workflow, and where would you not put it? Look for keys kept out of source control, shared sheets and screenshots, and held in the team’s approved configuration or the tool’s credential feature. Accept ‘I would ask which store the team approves’ as a good start.
  6. A teammate suggests pasting customer emails into an online AI tool to save time. What do you say? Look for asking whether the tool is approved, what data it keeps and who can see it, and for checking with a senior or the data owner first. Accept a suggestion to try made-up data while approval is sought.
  7. Describe a workflow or script you built that connects two tools. A link or a recording is welcome; if the work is private, describe it instead. Look for the trigger, the steps and one thing that went wrong and how the candidate found it. Accept a spoken description or a screen recording instead of a link.

What to look for when you shortlist

  • A written map of the workflow: the trigger, the steps, the exceptions and the point where a person approves
  • A sheet of test inputs with the expected result beside the actual result, even for a practice workflow
  • Handling of repeated and failed items, such as an ID check, a retry limit or an alert to a named person
  • A language-model step with a check on its output, such as a fixed set of examples, a required format or a person reviewing unclear cases
  • Keys and tokens kept out of shared files and screenshots, with placeholders in the walkthrough
  • Plain statements of limits, such as ‘this step needs a person to approve it’ or ‘I tested it only with a small sample’

Mistakes to avoid in this job description

  • Promising ‘fully autonomous agents’ in the post: describe the first workflow the fresher will build and say where a person approves, because a language model can return a confident wrong answer.
  • Leaving out the human approval step for money, customer messages and personal data: list which steps wait for a named approver, and ask the refund question in screening to see how candidates think about it.
  • Sending customer data to a tool nobody approved: name the tools and data the fresher may use, say who approves new ones, and start the fresher on made-up or public sample data.
  • Listing n8n, Make, Zapier, LangChain and ‘agentic’ in one line of the post: name the first workflow, the tool your team uses and the person who owns the process, and move the other tools to good to have.
  • Not saying who watches the runs: name who receives the failure alert, say whether checks are in working hours or on a rota, and do not make a fresher the only after-hours contact. A failed run that nobody sees leaves work undone.

Questions about hiring an AI automation engineer

What should a fresher AI automation engineer know?

API and JSON basics, one automation tool or short script that connects two apps, careful testing and an understanding that a language model can be wrong. Add the habit of keeping a person in the loop for risky steps and writing a run note. Ask the candidate to walk you through one workflow they built, and accept a description if the data was private.

How is an AI automation engineer different from a generative AI engineer, an RPA developer or a software engineer?

Here the role means business workflows built from apps, APIs and language-model steps. The title ‘automation engineer’ can also mean software test automation or factory control systems. A generative AI engineer builds language-model features in code, such as document search. An RPA developer builds bots that work through the screens, files and email of existing applications. A software engineer builds and maintains products and the systems behind them. Scripting, testing and deployment overlap, so name the first workflow and the tools in the post.

Does a fresher AI automation engineer need to code?

Only as far as the workflows need it. Where a workflow has a code step, such as reshaping JSON or retrying a call, basic Python or JavaScript is enough; software-engineering depth is not needed. If your workflows are built entirely from low-code blocks, move scripting to good to have and test API and JSON basics instead.

Can a fresher build AI agents that act on their own?

Only within limits. A fresher can build a workflow in which a model chooses between a few allowed actions, with a person approving anything that spends money, messages a customer or changes a record. Start with draft-and-approve flows. Wording in a prompt does not reliably stop a model following instructions hidden in an email or document, so keep its permissions narrow. Widen them only after tests and the run log show the workflow behaves, and agree each change with the process owner.

How can I test a fresher AI automation engineer fairly?

Give a one-page description of a made-up process, such as sorting enquiry emails, with a few sample emails and a stated time limit. Share the marking criteria beforehand: a clear trigger and steps, an approval point and awkward test cases. Ask for a sketch and a test list on paper, so no tool account is needed. Use nothing from your real backlog, and do not use the answer in your work.

What project suits an AI automation intern?

A good intern project is one repeat task with a clear finish, such as labelling enquiry emails or copying form entries into a sheet. Give the intern sample data only, an account on the team’s tools, a named mentor who reviews the run log each week and a short demo at the end. Where the provider offers one, set a spending limit on any model key. On LineupX you can post the internship for free once your company is verified, and add screening questions to the post.

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