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.
