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