Role: Lead Generative AI Engineer
Experience: 8+ years
Location: Gurgaon / Pune
Mode of work :WFO
Must have
Gen AI experience -3Y
Agentic Ai experience -1Y
Must have team leading exp
Role Overview
We are looking for a Senior Gen AI Engineer with proven experience in building and deploying Generative AI and scaled ML systems. This role demands end-to-end ownership of AI solutions from model selection, fine-tuning, and pipeline design to deployment on cloud ML platforms (AWS SageMaker).
The ideal candidate will be capable of architecting multi-agent frameworks and MCP pipelines, integrating them into enterprise systems, and delivering scalable, production-grade AI solutions. Along with strong engineering and system design expertise, this role involves mentoring junior engineers, leading PoCs, and collaborating closely with product and research teams to shape the organization's GenAI roadmap.
Key Responsibilities
Solution Architecture & Design
- Architect and implement enterprise-grade generative AI pipelines (LLMs, RAG systems, diffusion models).
- Design and optimize multi-component pipelines (MCPs) and agentic frameworks for workflow automation.
- Evaluate and implement architectural trade-offs for scalability, latency, and cost efficiency.
Model Development & Scaling
- Lead the development of traditional ML and statistical models, integrating them with GenAI systems.
- Fine-tune LLMs, build RAG pipelines with vector databases (FAISS, Pinecone, PgVector), and optimize inference performance.
- Scale training and deployment on AWS SageMaker using distributed training and monitoring techniques.
Engineering & MLOps
- Implement CI/CD for ML using GitLab pipelines, ensuring reproducibility and automation across the model lifecycle.
- Develop observability, monitoring, and governance frameworks for deployed AI models.
- Collaborate with DevOps and Data Engineering teams to integrate AI services into production environments.
Leadership & Collaboration
- Mentor and guide junior Gen AI engineers; review code and establish best practices.
- Work with cross-functional teams (Product, Research, Data Engineering) to align AI solutions with business goals.
- Lead PoCs, pilots, and research initiatives to explore new frameworks and technologies.
Required Skills
- Proven experience in enterprise AI/GenAI solution deployment in production environments.
- Expertise in LangChain, LlamaIndex, and other orchestration frameworks.
- Exposure to distributed systems, GPU optimization, and cost-efficient model scaling.
- Programming & Modeling: Advanced proficiency in Python, PyTorch, TensorFlow, Hugging Face, and scikit-learn.
- Generative AI Expertise: Hands-on experience with LLMs, transformers, diffusion models, and RAG architectures.
- Agentic & MCP: Strong background in agent-based frameworks and multi-component pipelines.
- Scaling AI: Practical knowledge of parallel/distributed training, optimization, and scaled inference.
- Cloud ML: Deep expertise in AWS SageMaker (training, hyperparameter tuning, endpoints, monitoring).
- MLOps: Proficient in CI/CD, model lifecycle management, and monitoring tools such as GitLab, MLflow, and Kubeflow.
- System Thinking: Ability to design solutions considering throughput, latency, fault tolerance, and observability.
