Lead GEN AI Engineer

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.

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.