Lead Generative AI Engineer

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

Senior Gen AI Engineer

multi-agent frameworks

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

  .