Lead Generative AI Engineer

Key Responsibilities

• Define the architecture, engineering standards, and technical direction for enterprise GenAI platforms and applications.
• Architect scalable, secure, reliable, and cost-efficient AI platforms, infrastructure, and reusable AI services.
• Design and productionize advanced RAG pipelines, including ingestion, chunking, embeddings, vector retrieval, reranking, context management, and evaluation.
• Establish best practices for LLMOps, prompt management, agent orchestration, AI observability, model evaluation, guardrails, and AI governance.
• Lead the development of enterprise AI copilots, conversational BI solutions, AI-powered customer service assistants, recommendation engines, knowledge retrieval systems, and autonomous workflow agents.
• Design sophisticated multi-agent and tool-calling workflows integrated with enterprise APIs, databases, applications, and external services.
• Drive GenAI solutions from architecture and proof-of-concept through production deployment, scaling, monitoring, and optimization.
• Optimize latency, throughput, GPU utilization, inference cost, scalability, reliability, and model performance.
• Implement strategies for hallucination mitigation, AI evaluation, security, privacy, Responsible AI, and enterprise compliance.
• Evaluate emerging LLMs, AI frameworks, inference technologies, vector databases, and orchestration platforms to guide the enterprise AI technology roadmap.
• Mentor AI/ML and GenAI engineers and provide technical leadership across multiple AI initiatives.
• Partner with business, product, and technology stakeholders to convert high-impact business opportunities into scalable AI solutions.
• Contribute to enterprise AI strategy, architecture, governance, and technology roadmap.

Required Experience

• 7+ years of professional experience across AI/ML engineering, software engineering, distributed systems, enterprise platforms, or related areas.
• Proven experience architecting, developing, and deploying production-grade AI/GenAI systems at scale.
• Strong hands-on experience taking AI solutions from concept and architecture to production deployment and optimization.
• Previous experience leading technical teams, AI engineering squads, or providing architecture and engineering leadership.