New opportunity

Lead AI Engineer

EPAM Systems · Chennai, India

About this role

EPAM Systems is seeking a Lead AI Engineer for its offices in Chennai or Coimbatore, India. The role leads AI and machine learning initiatives, with a focus on developing and deploying generative AI applications that improve business outcomes.

Responsibilities

Build and launch scalable AI/ML models and GenAI solutions in production; develop LLM-powered applications and use prompt engineering to improve outputs.
Build and maintain ETL pipelines and data workflows. Deploy models through APIs and integrate them with enterprise applications.
Own the development, deployment, monitoring and refinement lifecycle, working with cross-functional teams to align solutions with business goals.

Requirements

8–13 years of broad IT experience, more than 8 years focused on AI engineering, and at least 1 year in a leadership role.
Strong Python programming; experience with ETL, data pipelines, ML/DL frameworks such as TensorFlow, PyTorch and Scikit-learn, and hands-on LLM development.
Familiarity with LangChain, LlamaIndex and Hugging Face; experience with Azure, AWS or GCP and MLOps tools such as MLflow, Kubeflow and Airflow. Strong API development and production model deployment experience.
English proficiency at B2 level or higher, particularly for technical communication.

Preferred qualifications

Experience with RAG architectures and vector databases; real-time or streaming data pipelines; scalable systems and microservices; Databricks and PySpark; or cloud-based data solutions and ETL pipelines.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI engineeringGenerative AIMachine learningDeep learningPythonETLData pipelinesTensorFlowPyTorchScikit-learnLarge language modelsPrompt engineeringLangChainLlamaIndexHugging FaceHugging Face TransformersAzureAWSGCPMLflowKubeflowAirflowMLOpsAPI developmentREST APIModel deploymentRAGVector databasesStreaming data pipelinesMicroservices architecture style

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Deliver production-ready AI solutions
The posting calls for scalable AI/ML and GenAI solutions launched in live environments. Prepare to explain technical decisions from development through deployment, monitoring and refinement.
Build effective LLM applications
LLM application development and prompt engineering to improve output quality are explicit duties. Prepare a concrete example of how you evaluated and improved an application's outputs.
Keep data workflows dependable
Building and sustaining ETL pipelines and data workflows is a stated responsibility. Be ready to discuss pipeline design, data quality and recovery from failures.
Integrate models with enterprise applications
The role deploys models through APIs and connects them with enterprise applications. Prepare to explain an API integration, including how you handled production reliability and scale.
Lead toward business outcomes
The role owns the solution lifecycle and works with cross-functional teams to align AI solutions with business goals. Prepare to describe how you communicated technical choices and connected delivery decisions to a business objective.

Likely challenges to think through

Balancing LLM quality with production constraints
Preparation hypothesis, not a confirmed EPAM problem: improving generated outputs may compete with latency, cost or scalability when an application goes live. Prepare an example showing how you evaluated output quality and chose acceptable operational trade-offs.
Maintaining reliable data as solutions evolve
Preparation hypothesis, not a confirmed EPAM problem: changing inputs or workflows can make dependable ETL and model refinement harder to sustain. Prepare to explain how you would detect data issues, recover a pipeline and decide when a model needs revisiting.
Reconciling stakeholder goals with technical ownership
Preparation hypothesis, not a confirmed EPAM problem: cross-functional requests may pull against deployment effort or ongoing maintenance. Prepare an example of how you would clarify the desired business result, explain trade-offs in accessible technical language and agree on a feasible delivery scope.

Practice your answers

Technical practice

How would you choose a Python ML/DL framework for an AI solution?

What to coverThe role calls for Python and experience with TensorFlow, PyTorch and Scikit-learn. Suggested preparation: explain how the problem, model type, experimentation needs and production constraints would guide your choice.

How would you design a scalable GenAI application for production?

What to coverThe posting calls for scalable GenAI solutions in live environments; scalable systems and microservices are listed as nice to have. Suggested preparation: describe the application boundaries, deployment approach and trade-offs among throughput, latency, reliability and cost.

How would you build and troubleshoot a dependable ETL pipeline?

What to coverBuilding and sustaining ETL pipelines and data workflows is a stated responsibility. Suggested preparation: cover data validation, failure recovery and how you would trace a downstream model issue back to its input data.

How would you structure an LLM-powered application?

What to coverThe role calls for hands-on LLM development and familiarity with LangChain, LlamaIndex and Hugging Face. Suggested preparation: explain which application components you would build directly or with a framework, and how you would evaluate that choice.

How would you improve and evaluate prompt quality?

What to coverThe posting explicitly calls for prompt engineering to improve AI-generated outputs. Suggested preparation: describe a repeatable way to compare prompts against representative tasks, including how you would detect regressions rather than relying on a few examples.

How would you expose a model through an API for an enterprise application?

What to coverAPI development, production model deployment and enterprise integration are stated parts of the role. Suggested preparation: discuss interface design, handling failures and coordinating changes between the model service and its consumers.

How would you manage a model from deployment through refinement?

What to coverThe role owns development, deployment, tracking and refinement, and names cloud platforms and MLOps tools such as MLflow, Kubeflow and Airflow. Suggested preparation: explain what you would track, how you would detect declining performance and how findings would lead to a controlled update.

When would you use RAG and a vector database?

What to coverRAG architectures and vector databases are listed as nice to have, not required. Suggested preparation: explain retrieval, grounding and evaluation trade-offs, including when a simpler LLM application may suffice.

How would a streaming data pipeline change your design?

What to coverReal-time or streaming pipeline design is a nice-to-have area in the posting. Suggested preparation: compare it with batch processing in terms of freshness, operational complexity and recovery from delayed or failed data.

When would Databricks and PySpark be useful for the data workflow?

What to coverDatabricks and PySpark are listed as nice to have alongside cloud-based data and ETL work. Suggested preparation: explain when distributed processing would justify its complexity and when a simpler pipeline would be adequate.

Behavioural practice

Tell me about a time you led an AI initiative through delivery.

What to coverThe posting calls for leadership and ownership across development and rollout. Suggested preparation: use a real situation, your specific decisions and actions, and the resulting delivery or business outcome.

Describe a time you aligned an AI solution with a business goal.

What to coverWorking with cross-functional groups to achieve business results is a stated responsibility. Suggested preparation: explain the stakeholders' differing priorities, how you agreed on a goal and what the result showed.

Tell me about a production AI issue you helped resolve.

What to coverThe role includes production deployment, tracking and refinement. Suggested preparation: give the situation, how you investigated and coordinated the response, and what changed afterward; use an example you actually experienced.

Describe a time you explained a technical AI trade-off to a mixed audience.

What to coverThe posting emphasizes cross-functional work and technical communication in English. Suggested preparation: describe the audience, how you made the trade-off understandable, the decision reached and its result.

Sharpen your knowledge

Topics to prepare

Production AI lifecycle
Review how an AI/ML or GenAI solution moves from development through deployment, monitoring and refinement. Exercise: outline the decisions and checks at each stage for an enterprise application; the posting assigns ownership across this lifecycle.
LLM output quality and prompt engineering
Revise how prompt structure and evaluation affect generated outputs. Exercise: compare two prompts for the same business task and explain how you would judge whether the revised output is better; both hands-on LLM work and prompt engineering are stated requirements.
ETL and data workflow design
Review how to make data pipelines dependable as inputs change or processing fails. Exercise: sketch an ETL workflow, its failure points and a recovery approach; building and sustaining ETL pipelines is an explicit responsibility.
Cloud deployment and MLOps
Review the roles of cloud infrastructure, workflow orchestration, tracking and monitoring in production model delivery. Exercise: explain a deployment-and-refinement plan using one of Azure, AWS or GCP and relevant capabilities of MLflow, Kubeflow or Airflow; the posting names these platforms and tools.
Technical leadership and business alignment
Review how to translate a business goal into an AI delivery plan for cross-functional teams. Exercise: give a concise technical explanation of a proposed solution, its trade-offs and how its outcome would be assessed; the role calls for leadership, collaboration and strong technical communication in English.
RAG and vector database design
As optional preparation, review how retrieval and a vector database can support an LLM application. Exercise: sketch a retrieval-to-generation flow and explain when it may be preferable to prompting without retrieval; RAG and vector databases are listed as nice to have, not requirements.

Skills to revise

Python ML/DL implementation
Implement a small model workflow in Python with a relevant framework such as Scikit-learn, PyTorch or TensorFlow, then explain the training and inference steps. Strong Python and ML/DL framework experience are explicit requirements.
ETL pipeline construction
Build a small Python ETL workflow that transforms input data, handles an invalid record and produces a repeatable output. This rehearses the stated hands-on ETL and data-pipeline work.
LLM application development
Prototype an LLM-powered task using a framework named in the posting, such as LangChain, LlamaIndex or Hugging Face. Test two prompt versions and record how the outputs differ; the role requires hands-on LLM development and prompt engineering.
API-based model delivery
Expose a model or LLM application through an API and describe how an enterprise application would call it. Rehearse the steps for deploying it on a stated cloud platform; API integration and production deployment are core responsibilities.
MLOps workflow practice
Walk through a small experiment or pipeline using a named tool such as MLflow, Kubeflow or Airflow, and show what you would track before refining the deployed solution. The posting requires capability with MLOps tooling and lifecycle monitoring.

Your preparation checklist

Build a requirements evidence sheet
Map dated experience and specific examples to the posted 8–13 years in IT, more than 8 years in AI engineering, at least 1 year in leadership, and the technical requirements. Leave gaps clear rather than implying experience you cannot substantiate.
Prepare a production GenAI project walkthrough
Outline one relevant project from business goal to launch: your contribution, Python and LLM choices, prompt improvements, API and enterprise integration, production challenges, and observed results. Use only outcomes you can substantiate.
Document a data and ML implementation
Prepare a diagram or brief walkthrough of an ETL pipeline and data workflow you worked on, including reliability decisions and any relevant use of TensorFlow, PyTorch, or Scikit-learn.
Practice a hypothetical solution design
As preparation—not a stated interview exercise—sketch a scalable LLM application covering data flow, API deployment, cloud choices, monitoring, and refinement. Include RAG, vector databases, or streaming only where useful; the posting lists these as preferred.
Gather deployment and MLOps evidence
Select concrete examples of production deployment and monitoring, including Azure, AWS, or GCP and MLflow, Kubeflow, or Airflow where you have used them. Be ready to explain your decisions and what changed after launch.
Rehearse a leadership explanation
Prepare a concise account of how you coordinated cross-functional work around a business goal, handled a technical trade-off, and communicated the result in English; the posting emphasizes leadership and technical communication.
Write questions for EPAM
Bring questions about the team’s current GenAI use cases, how business outcomes are measured, which data and deployment tools are in use, and what lifecycle ownership this lead role carries.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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