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, Langchain, 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 DataBricks, AWS bedrock, Microsoft Copilot Studio and Azure ML.
Strong leadership skill 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.

ML Engineer

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