Cognizant Recruitment 2026 – Python Gen AI Engineer

Cognizant

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Experienced4 days ago

ExperienceExperienced

QualificationB.E / B.Tech / M.E / M.Tech

Skills & Eligibility

Candidates applying for the Python Gen AI Engineer position should have a B.E, B.Tech, M.E, or M.Tech qualification and relevant professional experience in software and generative AI engineering.
The role is intended for experienced candidates rather than entry-level applicants. Practical experience building production-grade applications is particularly important because the job description explicitly distinguishes production software from simple notebooks and prototypes.
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Applicants should ideally have hands-on experience across Python development, agentic AI frameworks, LLM applications, APIs, software architecture, SDLC practices, AI evaluation, observability, and AI governance.
Strong Python proficiency is one of the most important requirements. Candidates should have practical experience developing production-grade software rather than limiting their experience to experimentation in notebooks or proof-of-concept applications.
Engineers should be comfortable writing maintainable Python code, structuring applications into reusable modules, handling errors, integrating APIs, writing tests, and developing services suitable for production environments.
Experience with agentic frameworks such as LangChain and LangGraph, or similar technologies, is required. Candidates should understand how these frameworks can be used to construct workflows involving LLMs, tools, memory, state, and multi-step reasoning processes.
The role requires a solid understanding of LLM application concepts, including prompting, tool calling, function calling, RAG, embeddings, context management, and memory systems.
Candidates should understand how prompt design affects LLM outputs and how to create reliable instructions for enterprise use cases.
AI agents frequently need to interact with external services, databases, APIs, or business applications. Candidates should understand how LLMs can select or invoke tools and how those interactions can be controlled safely.
RAG combines information retrieval with LLM generation to provide models with relevant external context. Candidates should understand concepts such as document ingestion, chunking, embeddings, vector search, retrieval, context injection, and response generation.
Understanding embeddings and context management is important for applications that need to retrieve relevant information and provide it to an LLM efficiently.
AI agents can use different memory approaches to retain relevant information during interactions or workflows. Candidates should understand how application memory affects agent behavior and system design.
The role expects AI engineers to follow established software engineering practices rather than treating AI applications as isolated experiments.
Continuous Integration and Continuous Deployment (CI/CD)
Version control
Code reviews
Automated and manual testing
Reusable code and frameworks
Software lifecycle management
Automation of engineering workflows
Candidates should also understand how AI agents can augment or automate parts of the SDLC, such as code assistance, test generation, documentation, analysis, or development workflows.
Candidates should possess a B.E, B.Tech, M.E, or M.Tech qualification.
Yes. Familiarity with agent evaluation and observability tooling such as LangSmith, tracing frameworks, or custom evaluation harnesses is required.

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