AI Engineer

TechDigital Group; Town of Texas (WI); Hybrid; USD 110,000 - 160,000; Full time

Job summary

Cognizant is seeking an AI Engineer to embed with client teams and identify high-impact AI opportunities. You will design and deploy AI agents, craft RAG pipelines, and integrate solutions across enterprise systems using APIs and gateways.

The role emphasizes collaboration, memory-augmented workflows, and the ability to explain AI trade-offs to stakeholders. Strong Python and LLM experience are required.

Qualifications

• 3–5 years of AI/ML experience.
• Master's degree in CS/AI/DS; PhD preferred.
• Strong production-grade Python programming skills.
• Hands-on with LLMs (GPT, Claude, Gemini) and prompt engineering.
• Experience designing and deploying RAG pipelines (embeddings, vector DBs, reranking).
• Familiarity with LangGraph, LangChain, CrewAI, Google ADK.
• Working knowledge of REST APIs, cloud platforms (AWS/Azure/GCP), Git, Docker.
• Strong analytical, problem-solving, and communication skills.
• Consultative mindset for ambiguous environments.

Responsibilities

• Embed with client domain teams to identify high-value AI opportunities.
• Design, build, and deploy AI agents with memory and tool use.
• Build retrieval-augmented generation pipelines grounded in client data.
• Integrate AI solutions with client enterprise systems via APIs and gateways.
• Define success metrics and establish continuous benchmarking.
• Run workshops and pair-program with domain engineers.
• Surface platform gaps back to Cognizant teams for reusable improvements.

Skills

Production-grade Python

LLMs & prompt engineering

Agentic workflow design

RAG pipelines design

REST APIs

Cloud platforms (AWS/Azure/GCP)

Git & Docker

Analytical thinking & communication

Consultative mindset

Education

Master's degree in CS/AI/DS

PhD preferred

Related field MS/PhD

Tools

LangGraph

LangChain

CrewAI

Google ADK

Embeddings & vector databases

APIs & integration

Job description

Job Description:

• 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-haves:

• 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.