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AI/ML Engineer Job Description

An AI/ML engineer job description for teams productionising machine learning and generative AI features. It focuses on model serving, MLOps, LLM-based applications, evaluation and reliability rather than research alone.

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[Company Logo][Company Name]

JOB DESCRIPTION – AI/ML ENGINEER

Job Title:: AI/ML Engineer
Department:: Engineering – AI
Reports To:: AI Lead / CTO
Location:: [Work Location]
Employment Type:: Full-time, Permanent
Experience:: 3–6 years in ML engineering
Job Code:: [Reference Number]

About [Company Name]

[Company Name] is [one-line description of your business, products/services and team size]. Office address: [Company Address].

Job Summary

The AI/ML Engineer will design, build and operate production machine learning and generative AI systems for [Product] at [Company Name], ensuring models are accurate, reliable, cost-efficient and responsibly used.

Key Responsibilities

Build training and inference pipelines
Deploy models as scalable APIs using [Serving Framework]
Develop LLM-based features using retrieval-augmented generation
Set up vector databases and embeddings pipelines
Design offline and online evaluation for model quality
Implement MLOps: versioning, CI/CD, monitoring and drift alerts
Optimise latency and inference cost (quantisation, batching, caching)
Collaborate with data scientists and product teams
Apply data privacy and responsible AI safeguards
Document systems and runbooks

Required Skills

Python and ML frameworks (PyTorch / TensorFlow)
LLM APIs and open-source models
MLOps tools (MLflow, Kubeflow or similar)
Docker, Kubernetes, cloud GPU
Software engineering practices

Qualifications & Experience

B.E. / B.Tech / M.Tech in CS or related field
3–6 years in ML / software engineering

Preferred Skills (Good to Have)

Fine-tuning experience
Computer vision or speech
Indian-language NLP

Key Performance Indicators (KPIs)

KPI	Target	Review Frequency
Model latency (p95)	Under [Number] ms	Monthly
Evaluation score on benchmark set	[Score]+	Per release
Inference cost per request	Below ₹[Amount]	Monthly
Model incidents	Below [Number] per quarter	Quarterly

Salary Range

₹[Minimum CTC] – ₹[Maximum CTC] per annum (CTC), depending on experience and skills. Final offer will show the fixed and variable split and approximate in-hand salary.

Benefits

Provident Fund (EPF) and ESI as applicable, gratuity as per law
[Number] days of paid leave plus public holidays as per company policy
[Health insurance / group mediclaim cover]
GPU / research budget

How to Apply

Send your CV and links to deployed ML or GenAI projects. Send your application to [HR Email] with the subject line "Application – AI/ML Engineer – [Your Name]" or apply through [Application Link]. Last date to apply: [Date].

Prepared by
[HR Name]
Human Resources
Date: [Date]

Approved by
[Reporting Manager]
Engineering – AI
Date: [Date]

Download Word (.docx) — free

What this template includes

A job summary that explains why the ai/ml engineer role exists and where it sits in Engineering – AI
10 role-specific responsibilities, starting with build training and inference pipelines
Required skills such as python and ml frameworks (pytorch / tensorflow) and llm apis and open-source models, so screening criteria are clear
Qualifications and experience (3–6 years in ML engineering) stated up front to filter applications
A KPI table, e.g. model latency (p95), that later becomes the appraisal yardstick
Salary range, benefits and how-to-apply sections with placeholders ready to fill

When to use it

Shipping GenAI features such as chatbots or document search
Moving models from notebooks to production
Setting up MLOps and model monitoring
Building computer vision or NLP services

How to customise this template

1Replace [Company Name], [Work Location] and the reporting line (AI Lead / CTO) with your actual details
2Trim or reorder the responsibilities so the top three reflect what the ai/ml engineer will spend most time on
3Specify whether the focus is classical ML, deep learning, or LLM applications, and the infrastructure used
4Fill in the salary range as annual CTC in ₹ and decide whether to show it publicly or only on request
5Set realistic targets in the KPI table with the hiring manager before the role goes live

HR tips

Separate this role from data scientist by stressing production and reliability
Include evaluation and cost metrics for LLM features
Mention data privacy and responsible AI expectations

For HR & hiring managers

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Frequently asked questions

What does an AI/ML engineer do?+

An AI/ML engineer builds, deploys and maintains machine learning and AI systems in production, including data pipelines, model serving and monitoring.

How is ML engineer different from data scientist?+

Data scientists focus on modelling and analysis; ML engineers focus on productionising models reliably at scale.

Which skills are needed for GenAI roles?+

Python, LLM APIs and open-source models, retrieval-augmented generation, vector databases, prompt and evaluation design, and MLOps.

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