Cognizant – Python Gen AI Engineer

The Python Gen AI Engineer role at Cognizant is focused on building production-ready Generative AI applications, intelligent agents, and enterprise AI solutions using Python and modern LLM technologies. Based in Chennai with a hybrid work model, the position is part of Cognizant's Technology & Engineering organization and is designed for engineers who can move beyond experimentation and develop reliable software for real-world use cases. The role requires strong Python development skills along with practical experience in agentic AI frameworks such as LangChain and LangGraph. Engineers will work with core LLM concepts including prompt engineering, tool and function calling, Retrieval-Augmented Generation (RAG), embeddings, context management, and memory systems to create intelligent applications that can interact with enterprise systems and data.

A major focus of this position is designing modular, scalable, and platform-agnostic AI architectures rather than creating solutions tightly dependent on one LLM provider or cloud platform. The engineer will contribute to API-driven applications, microservices, reusable frameworks, and SDKs that make AI capabilities easier to integrate across different enterprise environments. The role also requires a solid understanding of software development lifecycle practices, including version control, code reviews, automated testing, and CI/CD. This is particularly important when developing AI agents because production GenAI systems require more than a working prompt or prototype—they need structured engineering, maintainability, testing, monitoring, and reliable integration with existing software ecosystems.

The position also places significant emphasis on AI evaluation, observability, and governance. Engineers are expected to understand how to evaluate agent behavior, trace application execution, monitor AI workflows, and identify problems using tools such as LangSmith, tracing frameworks, or custom evaluation harnesses. At the enterprise level, AI systems must also operate within appropriate governance controls, making knowledge of guardrails, access control, auditability, and human oversight important. Cognizant positions itself as an AI Builder focused on helping enterprises translate AI investments into practical business value, so this role provides an opportunity to work on AI engineering problems where software architecture, LLM capabilities, automation, and responsible AI practices come together.

Roles & Responsibilities

1. Develop Production-Grade Python Applications
   Build robust Python-based software for Generative AI use cases, focusing on clean architecture, maintainability, reliability, and production readiness rather than limiting development to notebooks or experimental prototypes.
2. Build Agentic AI Solutions
   Design and implement intelligent agents capable of reasoning through workflows, using tools, calling functions, accessing information, and completing multi-step tasks with appropriate controls.
3. Work With LangChain & LangGraph
   Use agentic frameworks such as LangChain, LangGraph, or comparable technologies to orchestrate LLM workflows, tools, memory, retrieval components, and application logic.
4. Implement LLM Application Patterns
   Develop solutions using prompting, tool/function calling, RAG, embeddings, context management, memory systems, and other foundational techniques required for reliable LLM-powered applications.
5. Design Platform-Agnostic Architectures
   Create modular AI architectures that can work across different LLM vendors and cloud environments, reducing unnecessary dependency on a single technology provider.
6. Develop APIs & Microservices
   Design and implement APIs and microservices that expose AI capabilities to enterprise applications while maintaining reusable, extensible, and well-structured interfaces.
7. Build Reusable Frameworks & SDKs
   Develop common components, frameworks, libraries, or SDKs that allow AI capabilities to be reused across multiple applications and accelerate enterprise GenAI development.
8. Apply Software Engineering Practices
   Follow established SDLC practices involving Git/version control, code reviews, testing, CI/CD pipelines, documentation, and structured development processes throughout the AI application lifecycle.
9. Evaluate AI Agent Performance
   Develop or use evaluation approaches to measure the quality, reliability, consistency, and effectiveness of AI agents and LLM-powered workflows.
10. Implement AI Observability
    Use tools such as LangSmith, tracing frameworks, or custom evaluation and monitoring solutions to understand agent execution, identify failures, and improve application performance.
11. Implement AI Governance Controls
    Apply appropriate guardrails, access controls, auditability mechanisms, and human-oversight practices to help ensure AI applications operate responsibly within enterprise environments.
12. Collaborate on Enterprise AI Solutions
    Work with engineering and technology teams to translate business requirements into scalable AI solutions while contributing to Cognizant's broader enterprise Generative AI initiatives.

Requirements & Eligibility

1. Strong Python Programming
   Candidates should have strong proficiency in Python and practical experience developing production-grade software. The role specifically expects engineering experience beyond basic scripts, academic projects, or notebook-only experimentation.
2. Generative AI & LLM Knowledge
   A solid understanding of Large Language Models and modern GenAI application patterns is essential, including prompting, tool calling, RAG, embeddings, context management, and memory systems.
3. Agentic AI Frameworks
   Practical experience with LangChain, LangGraph, or similar agentic frameworks is required. Candidates should understand how these frameworks can be used to construct multi-step AI workflows and tool-enabled agents.
4. RAG & Vector-Based Retrieval
   Candidates should understand Retrieval-Augmented Generation and embedding-based retrieval concepts, including how relevant external or enterprise information can be supplied to LLM applications.
5. API & Microservices Development
   Experience designing APIs, microservices, reusable components, or extensible software frameworks is important because the role involves integrating GenAI capabilities into larger enterprise technology environments.
6. Software Development Lifecycle
   Strong knowledge of SDLC practices such as version control, code review, testing, CI/CD, and structured software development is expected. Candidates should understand how these practices apply specifically to AI-enabled applications.
7. Modular Architecture
   Candidates should be capable of designing modular and platform-agnostic solutions that are not unnecessarily tied to a single LLM provider, cloud platform, or technology stack.
8. AI Evaluation & Observability
   Familiarity with LangSmith, tracing systems, custom evaluation harnesses, or comparable observability technologies is valuable for monitoring and evaluating AI agents and LLM applications.
9. AI Governance & Responsible AI
   Candidates should understand enterprise AI governance concepts including guardrails, access control, auditability, human oversight, and mechanisms for controlling AI application behavior.
10. Engineering & Problem-Solving Skills
    Strong analytical thinking, debugging ability, software engineering discipline, communication skills, and the ability to work on complex AI engineering problems are important for building reliable enterprise-grade Generative AI solutions.

Expected Salary

Cognizant has not publicly disclosed the salary range for this specific Python Gen AI Engineer position. The official posting identifies the role as a Technology & Engineering position in Chennai but does not publish a compensation figure.

Considering the specialized requirements around Python, LLMs, RAG, LangChain, LangGraph, agentic AI, APIs, microservices, evaluation, observability, and AI governance, a realistic market-based expectation for an experienced candidate in Chennai would be approximately ₹10 lakh–₹20 lakh per annum, with compensation potentially higher for candidates possessing strong production GenAI and enterprise AI architecture experience. This is an estimated market range and not an officially confirmed Cognizant salary.

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