New opportunity

AI Engineering Manager

EPAM Systems · Hyderabad, India

About this role

EPAM Systems seeks a hands-on AI Engineering Manager to lead AI and machine learning engineers building enterprise AI-powered products and platforms. The manager serves as the team's senior technical authority while contributing directly to architecture, coding and system design. The posting lists offices in Gurgaon, Pune, Coimbatore, Chennai, Hyderabad, Jaipur and Bangalore, India.

Responsibilities

Guide the design and implementation of scalable, reliable and secure AI systems; perform hands-on development, code reviews and complex system architecture.
Modernize legacy systems with capabilities including RAG, code generation and multi-agent workflows. Oversee AI projects from conception and experimentation through production deployment and optimization.
Establish software-development practices covering agile methods, CI/CD and automated testing. Work with executive leadership and product management on AI strategy and the roadmap, including build-versus-buy decisions.
Manage budgets, resources and vendors; communicate progress and risks to technical and non-technical stakeholders. Lead hiring, onboarding, mentoring and career development for the engineering team.

Requirements

13–15 years of software engineering experience, with a significant portion in AI, machine learning or data science, plus 5–8 years leading or managing high-performing engineering teams.
Extensive hands-on expertise in Python, Java or C++; deep expertise in machine learning, deep learning, natural language processing and generative AI.
Experience building scalable AI infrastructure and MLOps/LLMOps pipelines on cloud platforms such as AWS, Azure and GCP; strong background in system design, microservices architecture and API development.
Bachelor's or Master's degree in Computer Science, Engineering or a related field.

Preferred

A PhD; experience as a hands-on 'player-coach'; delivery of large-scale, complex AI projects; strong problem-solving, communication and interpersonal skills, including the ability to navigate ambiguity.

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

Skills for this role

AI solution engineeringPythonJavaC++.NETArchitecture designMachine learningDeep learningNatural language processingGenerative AIRAGCode generationMulti-agent workflowsAI infrastructureMLOpsLLMOpsAWSAzureGCPSystem designMicroservices architectureAPI developmentAgile methodologiesCI/CDAutomated testingTechnical leadershipTeam managementBudget managementVendor managementCommunication

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

Own AI system architecture and contribute hands-on
The posting makes this manager the team's senior technical authority for scalable, reliable and secure AI systems, while expecting significant time spent coding, reviewing code and designing complex systems.
Modernize legacy systems with AI
The role calls for integrating capabilities such as RAG, code generation and multi-agent workflows into legacy systems, drawing on system design, microservices and API experience.
Take AI projects through production
The manager oversees work from conception and experimentation through deployment and optimization, and establishes agile, CI/CD and automated-testing practices. The requirements also emphasize scalable AI infrastructure and cloud MLOps/LLMOps pipelines.
Connect the roadmap to business and resource decisions
The posting calls for working with executive leadership and product management on AI strategy, making build-versus-buy decisions, managing budgets and vendors, and communicating progress and risks.
Grow and support the engineering team
The manager leads AI and machine learning engineers and oversees mentoring, hiring, onboarding and career development.

Likely challenges to think through

Balancing hands-on work with management
Preparation hypothesis, not a reported EPAM problem: significant coding and architecture time could compete with mentoring, hiring and delivery oversight. Prepare an example of how you set technical direction and delegate without becoming a bottleneck.
Adding AI to legacy systems without weakening reliability
Preparation hypothesis: RAG or agent-based capabilities may introduce integration and security trade-offs in existing systems. Prepare to explain how you would stage an integration, test it and assess reliability before broader deployment.
Choosing when to build or buy
Preparation hypothesis: AI tool decisions may trade speed of delivery against cost, control and long-term maintainability. Prepare a decision framework that connects technical options to the roadmap, budget and vendor considerations named in the posting.

Practice your answers

Technical practice

How would you design a scalable, reliable and secure enterprise AI system?

What to coverSuggested preparation: Explain how you would separate components, manage access to data and models, and plan for failures and changing demand. Discuss the trade-offs you would assess before choosing an architecture.

How would you add a RAG capability to a legacy system?

What to coverSuggested preparation: Describe an integration path that preserves existing functionality while connecting retrieval and generation. Explain how you would evaluate answer quality, data access and operational reliability before expanding its use.

How would you evaluate code generation as part of legacy-system modernization?

What to coverSuggested preparation: Explain where generated code might help and where human review remains necessary. Discuss how you would validate correctness, security and maintainability against the existing system.

When would a multi-agent workflow be preferable to a simpler AI workflow?

What to coverSuggested preparation: Compare the potential value of dividing work among agents with the added coordination and reliability costs. Explain how you would test whether the extra complexity improves the outcome.

How would you choose and evaluate an ML, deep-learning or generative-AI approach to an NLP problem?

What to coverSuggested preparation: Explain how task requirements and available data would shape the choice of approach. Discuss evaluation criteria and trade-offs such as quality, latency, cost and maintainability.

How would you design an MLOps or LLMOps pipeline for a cloud-hosted AI product?

What to coverSuggested preparation: Walk through the path from experimentation to deployment, monitoring and updates on a cloud platform. Explain how you would make changes reproducible and detect when a production system needs attention.

How would you structure microservices and APIs around an AI capability?

What to coverSuggested preparation: Describe service boundaries and API contracts that would allow an AI component to evolve without unnecessarily disrupting other systems. Discuss trade-offs in latency, failure handling and versioning.

What would you look for in a hands-on code review of an AI system?

What to coverSuggested preparation: Cover code quality, tests and the implications of changes for reliability and security. Explain how you would give actionable feedback while retaining responsibility for difficult architectural decisions.

How would you move an AI project from experiment to production and then optimize it?

What to coverSuggested preparation: Identify what evidence would justify moving beyond an experiment and what automated testing and CI/CD practices would support release. Discuss how production observations would inform subsequent optimization.

How would you make a build-versus-buy decision for an AI tool or platform?

What to coverSuggested preparation: Compare fit with product goals, integration effort, cost, control and operational risk. Explain what evidence you would bring to product and executive stakeholders when recommending a choice.

Behavioural practice

Tell me about a time you balanced hands-on engineering with leading a team.

What to coverSuggested preparation: Use a real situation, your actions and the result to show how you chose where to contribute technically and where to delegate. Include how your approach supported the team's work rather than creating a bottleneck.

Describe a time you shaped a technical roadmap amid ambiguity.

What to coverSuggested preparation: Give the situation, how you worked with product or business leaders to clarify priorities, and the result. Explain how you handled uncertainty without presenting assumptions as settled facts.

Tell me about a complex project where you had to communicate a material risk.

What to coverSuggested preparation: Describe the situation, the actions you took to explain the risk to technical and non-technical stakeholders, and the result. Show how that communication affected a decision or delivery plan.

Describe a time you had to make a difficult resource or vendor decision.

What to coverSuggested preparation: Set out the budget or capacity constraints, your decision-making actions, and the result. Explain the trade-offs you communicated and how you monitored the decision's impact.

Tell me about a time you helped an engineer grow while strengthening the team.

What to coverSuggested preparation: Use a real situation, your mentoring, onboarding or development actions, and the result. Explain how you balanced individual growth with team needs.

Sharpen your knowledge

Topics to prepare

ML, deep learning, NLP and generative AI
The posting requires deep expertise in all four areas. For revision, compare two approaches to an enterprise language task, explaining model choice, evaluation criteria and likely failure modes.
AI-enabled legacy modernization
The role explicitly covers RAG, code generation and multi-agent workflows. For revision, take a hypothetical legacy workflow and explain when each capability would help, when it would add unnecessary complexity, and how you would assess its output.
Scalable AI system architecture
The posting calls for reliable, secure AI systems and a strong background in microservices and APIs. For revision, sketch a service architecture and explain its scaling, failure-handling and security trade-offs.
Production AI lifecycle
The role spans experimentation, deployment and optimization, with cloud-based MLOps/LLMOps pipelines required. For revision, walk through how a hypothetical AI change moves from experiment to production and how you would decide whether to retain or revise it.
Engineering strategy and leadership
The manager works with product and executive leadership on roadmaps and build-versus-buy decisions, while managing resources, vendors and the team. For revision, prepare a hypothetical AI initiative and explain its business trade-offs, staffing choices, risks and mentoring approach.

Skills to revise

Hands-on coding
Significant coding time and expertise in Python, Java or C++ are explicit. As practice, implement a small AI-facing service component in one of those languages, then review it for correctness, maintainability and error handling.
API and microservice design
System design, microservices and API development are required. As practice, write an API contract for a hypothetical AI capability and trace a request through its services, including failure and access-control cases.
Testing and delivery pipeline design
The posting names CI/CD, automated testing and MLOps/LLMOps pipelines. As practice, draft a pipeline for an AI service that specifies test stages, deployment checks and a response to a failed release.
Technical code review
The manager is expected to conduct code reviews and act as senior technical authority. As practice, review a sample AI-service change and write prioritized feedback on correctness, reliability, security and architectural fit.
Stakeholder communication
The role requires explaining progress and risks to technical and non-technical stakeholders. As practice, deliver two short updates on the same hypothetical project: one covering engineering decisions and one covering business impact, risks and decisions needed.

Your preparation checklist

Map evidence to the requirements
Prepare a one-page inventory of examples covering software engineering, AI or ML work, team leadership, hands-on Python, Java or C++, and cloud AI infrastructure. Note the posting’s stated experience and degree requirements without assuming they are met.
Build an end-to-end project walkthrough
Choose a real AI project, if available, and outline your decisions from conception and experimentation through deployment and optimization. Identify your own architecture, coding and leadership contributions, plus measurable results and trade-offs.
Practice a legacy-system modernization design
Sketch how you would add a RAG capability to an existing system. Explain the microservices and API boundaries, data flow, security, reliability, scaling and testing choices; be ready to discuss when code generation or multi-agent workflows would be appropriate instead.
Prepare a hands-on technical discussion
Select code or a code-review example you can discuss without disclosing confidential material. Explain a consequential design or implementation choice in Python, Java or C++ and how you would review it as the team’s senior technical authority.
Outline a production AI delivery plan
Draw a sample MLOps or LLMOps pipeline on a cloud platform you know, covering automated tests, CI/CD, deployment, monitoring and optimization. Distinguish what you have implemented from what you would propose.
Document a people-leadership example
Prepare a specific account of hiring, onboarding, mentoring or career development, including how you balanced hands-on technical work with leading engineers. State the situation, your actions and the outcome.
Rehearse an executive-level decision
Prepare a brief build-versus-buy comparison for an AI tool or platform, with business alignment, budget, resource and vendor trade-offs. Practice explaining the recommendation and its risks to both technical and non-technical stakeholders.
Write targeted questions for EPAM Systems
Bring questions about the AI team’s current products and modernization priorities, expectations for hands-on work, ownership of the AI roadmap, and how success and delivery risks are assessed in this role.

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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