Lead GenAI Engineer

AscendionPune, MaharashtraOn-siteFull-timeSenior, 5–8 yearsListed 3 days ago

Apply now

About this role

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