AI Engineer

AI Solutions Architect

Embed with client domain teams to identify high-value AI opportunities, map pain points to platform capabilities, and validate feasibility before solutioning. Design, build, and deploy AI agents using agentic frameworks (LangGraph, CrewAI, Google ADK) with tool use, memory, structured outputs, and error recovery. Build retrieval-augmented generation (RAG) pipelines grounded in client enterprise data — designing context engineering and memory architectures for multi-turn and multi-agent workflows. Integrate AI solutions with client enterprise systems via APIs, MCP tool gateways, CRM, billing, and operational platforms — handling authentication, rate limiting, and production-grade error handling. Define success metrics in partnership with client stakeholders, build evaluation harnesses, and establish continuous benchmarking and quality monitoring for deployed AI systems. Serve as a trusted technical partner to client teams — run workshops, pair-program with domain engineers, and drive AI adoption and enablement on the ground. Surface platform gaps, friction, and feature requests back to Cognizant's AI architecture and engineering teams to improve reusable offerings.

Must Have's:

• 3-5 yrs experience
• Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field with 1–3 years of relevant experience. PhD preferred.
• Strong production-grade Python programming skills — not notebook-grade; experience building deployable, maintainable AI applications.
• Hands-on experience with LLMs (GPT, Claude, Gemini), prompt engineering, structured outputs, function calling, and agentic workflow design.
• Experience designing and deploying RAG pipelines — embeddings, vector databases, reranking, hybrid search, and retrieval optimization.
• Familiarity with agentic AI frameworks such as LangGraph, LangChain, CrewAI, Google ADK, or similar orchestration tools.
• Working knowledge of REST APIs, cloud platforms (AWS, Azure, or GCP), Git, Docker, and modern software development practices.
• Strong analytical, problem-solving, and communication skills with the ability to explain AI trade-offs to non-technical stakeholders.
• Consultative mindset — comfortable operating in ambiguous environments, discovering problems, and defining approaches independently.