Job Requirements

Lead GenAI Engineer – Enterprise & Product AI

Role Overview

We are looking for a Lead GenAI Engineer who will drive identification, architecture, implementation, adoption, and governance of GenAI solutions while enabling teams to leverage common AI capabilities, platforms, and best practices.

Key Responsibilities

• Partner with product teams, engineering, architecture, data science, and business stakeholders to identify and prioritize GenAI opportunities and use cases.
• Define and drive GenAI solution architecture, including LLMs, RAG, Agentic AI, MCP, AI agents, vector databases, knowledge graphs, and AI orchestration.
• Lead technical evaluation and selection of models, frameworks, tools, and AI platforms based on business and product requirements.
• Develop PoCs, prototypes, and production-ready GenAI solutions, ensuring scalability, security, performance, and maintainability. Must be hands-on with coding, solution development, and technical implementation.
• Establish reusable GenAI patterns, frameworks, components, and reference architectures across products.
• Provide technical guidance on prompt engineering, RAG, agent design, evaluation, guardrails, hallucination mitigation, and AI observability.
• Drive GenAI quality and evaluation practices, including accuracy, relevance, groundedness, latency, cost, and safety.
• Collaborate with cybersecurity, compliance, legal, and architecture teams to ensure responsible and compliant AI adoption.
• Stay current with emerging GenAI technologies and assess their applicability to healthcare and product engineering.
• Conduct technical workshops, knowledge-sharing sessions, and enablement programs to improve GenAI adoption and AI literacy.
• Mentor GenAI engineers and support product teams in building their GenAI capabilities.
• Represent the organization/product team in GenAI technical forums, innovation initiatives, and technology evaluations.

Required Skills and Qualifications

• Bachelor’s/Master’s degree in Computer Science, AI/ML, Engineering, or related field.
• 10–12 years of experience in software/AI engineering and development, including significant experience in Generative AI/LLMs.
• Strong hands-on experience with Generative AI, LLMs, RAG, and AI Agents.
• Preferred: Azure AI, Azure Cloud, or other relevant AI/cloud technology certifications; not mandatory.
• Knowledge of Azure AI / cloud AI services, vector databases, AI Search, APIs, and containerized deployments.
• Understanding of MCP, Agent-to-Agent communication, orchestration, and AI-native application patterns.
• Experience with LLM evaluation, observability, guardrails, security, and responsible AI.
• Ability to translate business/product problems into scalable GenAI solutions.
• Strong understanding of AI architecture, evaluation, security, and governance.
• Excellent technical leadership, communication, and stakeholder management skills.