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Cognizant Python Gen AI Engineer Hiring 2026 Freshers and entry level apply . This is a great opportunity for freshers and early-career professionals looking to kickstart their journey in a global IT services company. The position is based in India and eligible candidates are encouraged to apply online at the earliest. The detailed eligibility criteria and application process are given below. Also Check course section free
ABOUT COMPANY
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value by building full-stack AI solutions for our clients. Our deep industry, process and engineering expertise enables us to build an organization’s unique context into technology systems that amplify human potential, drive tangible outcomes and keep global enterprises ahead in a fast-changing world. See how at cognizant.ai or @cognizant.
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JOB DESCRIPTION
To design, develop, and implement enterprise-grade generative AI systems, we are looking for a production-focused Python Generative AI Engineer. You will serve as a liaison between cutting-edge large language models (LLMs) and practical software applications. By including Retrieval-Augmented Generation (RAG) pipelines and multi-agent orchestration frameworks into our core product ecosystem, you will go beyond notebooks and prototypes to create scalable, dependable, and secure AI systems.
Key Responsibilities
- Build Applications: Create LLM-powered applications and API integrations.
- implement RAG: Set up Retrieval-Augmented Generation pipelines and vector databases for enterprise data.
- Develop Workflows: Build agentic AI workflows using tools like LangChain or Hugging Face.
- Fine-tune Models: Adapt foundation models for specific business needs.
- Manage Production : Deploy AI systems with proper monitoring, guardrails, and cost optimization.
Required Technical Skills
- Strong proficiency in Python, with hands-on experience building production-grade software (not just notebooks/prototypes).
- Practical experience with agentic frameworks such as LangChain, LangGraph, or similar.
- Solid understanding of LLM fundamentals — prompting, tool/function calling, RAG, embeddings, context management, memory systems.
- Experience designing modular, platform-agnostic architectures (i.e., not tightly coupled to a single LLM vendor or cloud provider).
- Understanding of SDLC processes (CI/CD, version control, code review, testing) and how AI agents can augment or automate them.
- Experience with API design, microservices, and building extensible/reusable frameworks or SDKs.
- Familiarity with agent evaluation and observability tooling (e.g., LangSmith, tracing frameworks, custom eval harnesses).
- Strong grasp of governance concepts for AI systems — guardrails, access control, auditability, human oversight.
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