Lead GenAI Engineer

Bengaluru; Chennai, Tamil Nadu, India; Bangalore, Karnataka, India · On site · Posted 9/21/2026 Leaves the board in 6 days

Open in 3 locations. Pick one to apply to.

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Role Overview
We are hiring a Lead GenAI Engineer with 7–10 years of experience to drive the design, architecture, and delivery of enterprise-scale GenAI platforms. This role requires strong technical leadership and proven experience in building production-grade AI systems end-to-end.
Key Responsibilities
  • Define architecture and technical strategy for GenAI platforms and solutions
  • Lead design and implementation of complex RAG systems and agentic workflows
  • Drive adoption of LangChain / LangGraph and advanced LLM orchestration frameworks
  • Architect high-performance vector search and retrieval systems
  • Oversee deployment strategies, including scalable cloud-native architectures
  • Ensure reliability, observability, and governance of AI systems in production
  • Lead and mentor engineering teams; conduct design reviews and code reviews
  • Collaborate with stakeholders to align AI solutions with business goals
  • Evaluate and integrate new tools, models, and frameworks in the GenAI ecosystem
Required Skills
  • Expert-level proficiency in Python
  • Extensive experience with RAG, LLMs, and prompt engineering at scale
  • Strong expertise in LangChain / LangGraph and agent-based architectures
  • Deep experience with Vector Databases and retrieval optimization
  • Proven track record of deploying production-grade GenAI applications
  • Strong experience with cloud (AWS/GCP/Azure), Kubernetes, and microservices architecture
  • Solid understanding of system design, scalability, and distributed systems
Preferred Qualifications
  • Experience leading GenAI/AI transformation initiatives
  • Strong knowledge of LLMOps, MLOps, and governance frameworks
  • Exposure to fine-tuning, evaluation frameworks, and guardrails
  • Prior experience in client-facing or consulting roles


Common Must-Have Across All Roles
  • Must have worked on at least one productionized AI/GenAI application
  • Should have been involved in the end-to-end lifecycle:
  • Problem definition → Data → Model → RAG → Deployment → Monitoring
  • Strong ownership mindset and ability to work in fast-paced environments