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Machine learning engineer job description template

A ready-to-post job description for a fresher or intern machine learning engineer. Its screening questions test engineering habits, and the page draws a clear split from the data scientist role.

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In short

A fresher machine learning engineer turns trained models into working software. The work is tested Python for data pipelines, training jobs and prediction services, plus checks after launch. Ask for strong Python, Git, machine learning basics and one project that runs outside a notebook, such as a model behind an API. Code quality matters more than research depth.

Machine learning 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 machine learning engineer (fresher)

Machine learning 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

[Company name] is hiring a fresher machine learning engineer to join the [Team] team. You will help turn trained models into services that our apps can call: writing the code that prepares data, runs training jobs and returns predictions. You will work with senior engineers and data scientists, write tests, and learn how to monitor a model after launch. This role suits someone who enjoys coding as much as modelling.

What you will do

  • Write Python code that loads, validates and transforms data for model training, with unit tests.
  • Turn data scientists’ notebook code into clean, modular scripts and reusable functions.
  • Train and retrain models with scikit-learn, PyTorch or TensorFlow, using the team’s experiment-tracking setup.
  • Wrap a trained model in a simple API with FastAPI or Flask, and test its response time and error handling.
  • Package code with Docker and run it on a cloud server or managed service, guided by a senior engineer.
  • Version code in Git and use the team’s approved tools to record dataset versions, model artefacts, dependencies and experiment settings.
  • Record approved operational metrics and prediction summaries, keep sensitive values out of logs, and review errors with a senior engineer.
  • Run scheduled batch prediction jobs and report any failure with its cause.
  • Fix bugs in data and training pipelines, and write a short note on each cause and fix.
  • Submit small pull requests and respond to code review comments.

What we are looking for

  • Strong Python: functions, classes, error handling, virtual environments and packaging basics.
  • Data structures and algorithms at the level of a good B.Tech or BCA course.
  • Core machine learning concepts: supervised learning, loss, overfitting, train and test splits, and evaluation metrics.
  • Hands-on use of at least one framework: scikit-learn, PyTorch or TensorFlow.
  • Git and command-line comfort in a Linux or macOS terminal.
  • Working knowledge of REST APIs: calling one and building a simple one.
  • Basic SQL.
  • One project that goes beyond a notebook, such as a model behind an API, a small app or a scheduled script.

Good to have

  • Docker basics and a deployed demo, even on a free cloud tier.
  • Experience with experiment tracking tools such as MLflow.
  • Hands-on use of a large language model (LLM) API or Hugging Face models, including how you tested the outputs.
  • A project on AWS, Google Cloud or Azure.
  • Contributions to an open-source repository or a published package.

Who can apply

B.Tech, BE, M.Tech and MCA graduates in computer science, IT, AI and data science or electronics fit best. BCA and BSc (Computer Science) graduates with strong programming fit too. Coding ability and one project that runs outside a notebook matter more than the degree stream.

How to apply

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

2. Machine learning engineer internship

Machine learning 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 a machine learning intern to support our engineers on model pipelines. You will write and test small pieces of Python, run training jobs and help package a model for use in an application. This suits pre-final and final-year B.Tech, BCA, MCA and M.Tech students who code comfortably and have trained at least one model.

What you will do

  • Write Python functions for data cleaning and feature preparation, with simple tests.
  • Run training scripts with different settings and record the results in the team’s tracker.
  • Build a small demo API around an existing model and document how to call it.
  • Write a README and setup steps so that others can run a project.
  • Review approved, sanitised prediction logs for errors and unusual patterns, and report findings to your mentor.
  • Fix small bugs in existing pipelines through pull requests that a mentor reviews.

What we are looking for

  • Studying for, or recently completed, a B.Tech, BCA, MCA or M.Tech in computer science, IT or a related field.
  • Comfort writing and debugging Python scripts without help on simple tasks.
  • Basic understanding of one machine learning algorithm and how to evaluate it.
  • A GitHub profile or project link with code we can read.
  • Git and command-line basics.

Who can apply

B.Tech, BE, M.Tech and MCA graduates in computer science, IT, AI and data science or electronics fit best. BCA and BSc (Computer Science) graduates with strong programming fit too. Coding ability and one project that runs outside a notebook matter more than the degree 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 machine learning engineer, Associate ML engineer, AI engineer (fresher), Machine learning trainee, AI/ML developer, Associate software engineer (ML).

Screening questions to add to your job post

  1. You have a trained model saved as a file. How would you let another application get predictions from it? Looks for the idea of an API or a batch job. Weaker answers only describe retraining.
  2. Name one reason a model can score well in your notebook and still give wrong predictions inside an application. Good answers mention preprocessing done differently in training and serving, different input formats, library version changes or data that has drifted.
  3. How would you make sure you can reproduce a model you trained last month? Looks for version control, fixed random seeds, saved configuration, a dependency file and a record of which data was used.
  4. Share a link to a repository whose code runs outside a notebook, or describe one if the code is private, and say in one line how to run it. Tests engineering habits. A README, a requirements file and a clear run command show that the candidate has packaged work for other people.
  5. Name one signal you would monitor after deploying a model, and explain what it would tell you. Look for a split between service health, such as errors or response time, and prediction quality. A shift in input data can prompt a closer look. Comparing predictions with real outcomes helps judge model quality.
  6. How would you test a Python function that cleans phone numbers? Shows engineering thinking. Strong answers list edge cases such as empty values, spaces, country codes and letters, instead of checking one sample by eye.

What to look for when you shortlist

  • A repository with a README, a requirements file and a few tests, not only notebooks.
  • A model reachable through an API, script or small app that you can try yourself.
  • A commit history that shows bugs found and fixed, not one upload on the last day.
  • Comfort with Git branches, pull requests and the terminal.
  • Questions about inputs, failures and speed, which show the candidate sees the model as one part of a system.
  • Code that handles bad input such as empty values, wrong types and missing files.

Mistakes to avoid in this job description

  • Not saying where models will run: name the cloud platform, in-house setup or third-party service, and say which parts the fresher will support.
  • Writing a research-scientist JD with papers and new architectures for an engineering role: describe the code, tests and pipelines the fresher will own.
  • Expecting years of production experience from a fresher: ask for one project that runs outside a notebook, and teach the rest.
  • Skipping a coding test: engineers are judged on code, so add a short coding task next to the machine learning questions.
  • Leaving monitoring out of the JD: say who watches the model after launch, so applicants know whether the role is build-only or build-and-run.

Questions about hiring a machine learning engineer

What is the difference between a machine learning engineer and a data scientist?

Ask who owns the model after launch. A data scientist explores data and shows that a model can work. A machine learning engineer makes it run reliably for users, with tested code, an API or batch job, versioning and monitoring. In a small team a fresher may cover both, so name in the JD which half comes first.

What should a fresher machine learning engineer know?

Good Python, Git and the command line, SQL basics, and the core ideas of supervised learning with one framework such as scikit-learn or PyTorch. The deciding extra is a project that runs outside a notebook, for example a model behind a small API. Docker and cloud experience are useful, but a new hire can learn them on the job.

Is an AI engineer the same as a machine learning engineer?

Titles overlap and companies use them loosely. An AI engineer role often means building applications on large language model (LLM) APIs, with prompts, retrieval and evaluation. A machine learning engineer role more often means training and serving your own models. Do not rely on the title: describe the first project and list the tools it needs.

How do I test a fresher machine learning engineer?

Set a short coding task, such as cleaning a small dataset and exposing a trained model through a function or an endpoint, and ask for tests. Read the code for naming, structure and error handling before you look at the model score. Follow with one design question, for example how they would retrain the model every week.

Do freshers need cloud or Docker experience for this role?

Not as must-haves. Freshers can learn deployment on the job, and asking for it narrows your pool. Ask instead for a project that proves they can package and run code: a README, a requirements file and an endpoint someone else can call. If your stack depends on one cloud, name it under good to have.

Where do I find fresher machine learning engineers?

Computer science, AI and data science departments are the main source, so contact placement cells and post the role where students look. Listing jobs and internships on LineupX is free, with no hidden charges, and each job is reviewed before it goes live. Add a screening question asking for a repository link, so you see code before the first call.

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