AI/ML engineer

Responsibilities

Product & Business Partnership

• Co-own problem discovery with Product/Business: translate objectives into well defined, testable problem statements, user journeys, and technical hypotheses.

• Challenge assumptions and requirements using user insights, data realities, feasibility constraints, and system impact; recommend alternatives when they better achieve outcomes.

• Define and track success metrics (e.g., adoption, time saved, conversion, accuracy/quality, latency, reliability, cost-to-serve, risk reduction) and drive iteration when targets aren't met.

• Communicate clearly with executive, business, and engineering audiences - articulating trade-offs, decisions, and rationale.

Solution Leadership

• Lead end-to-end solution for GenAI products: APIs, services, data pipelines, orchestration, LLM integration, retrieval, tool-calling, and UI/UX touchpoints as needed.

• Make disciplined architectural trade-offs across performance, reliability, cost, extensibility, maintainability, and time-to-value.

• Design for enterprise realities: identity/access, data residency, compliance constraints, multi-environment deployments, and integration with core systems.

• Drive platform thinking prioritize reusable components (libraries, templates, shared services) over one-off builds.

Agentic & Full-Stack GenAI Applications

• Build and scale agentic GenAI applications that solve multi-step workflows on cloud platforms such as AWS and Azure.

• Define and implement robust LLM patterns (prompting, RAG, tool orchestration, validation, and evaluation).

• Implement LLMOps and lifecycle governance for models and prompts, including versioning, evaluation and monitoring.

• Ensure solutions meet enterprise standards for quality, reliability, latency, and cost.

• Embed Responsible AI, security, and compliance by design, partnering with Risk, Legal, and Security.

Engineering Excellence & DevOps

• Provide hands-on leadership across the SDLC: design reviews, coding standards, testing strategy, code reviews, security reviews, and operational readiness.

• Establish robust CI/CD and standardized environments for services and AI components utilizing cloud services.

• Ensure operational excellence through observability, incident management, reliability practices, and cost optimization.

Required Knowledge:

• Proven track record in designing and building GenAI solutions, with a strong expertise in prompt engineering, LlamaIndex, Lang chain, RAG, Agentic AI workflow,

• Knowledge graph, AI red teaming libraries, LLM monitoring and evaluation.

• Over 7+ years of experience in the software development lifecycle as a developer, with a focus on writing production code.

• Must have hands-on experience working with software development toolkits, and devOps automation like Kubernetes, Airflow, Jenkins, Jira, Confluence and Git.

• Excellent programming skills in Python and proficiency in PySpark.

• Experience in Data Bricks, AWS bedrock, Microsoft Copilot Studio and Azure ML.

• Strong leadership skills with ability to influence the thought process and drive alignment.

• Excellent communication skills and a demonstrated ability to collaborate effectively with cross-functional teams.

• Self-motivated and highly driven individual who can thrive in ambiguous requirements.

About Cognizant:

Cognizant (Nasdaq: CTSH) engineers modern businesses. We help our clients modernize technology, reimagine processes and transform experiences so they can stay ahead in our fast-changing world. Together, we're improving everyday life. See how at www.cognizant.com or @cognizant.