AI/ML Lead OR Architect

AI/ML Lead Architect

production-grade AI/ML and GenAI solutions

Key Responsibilities

1. Architecture & System Design

• Architect end-to-end

  AI/ML, LLM, and Agentic AI systems

  including data, model, API, and deployment layers.
• Design scalable microservices-based AI architectures using

  Python, FastAPI, Docker, Kubernetes

  .
• Build

  multi-agent AI systems

  using LangChain, CrewAI, AutoGen, or LangGraph.
• Create architectural diagrams, solution blueprints, and design documents.
  

2. AI/ML Development

• Build and deploy ML/DL models using

  PyTorch, TensorFlow, Hugging Face Transformers

  .
• Implement

  Generative AI + LLM-based applications

  (GPT, LLaMA, Mistral, Claude).
• Research, fine-tune, and optimize LLMs & embeddings for real-world applications.
  
• Implement

  RAG pipelines

  with Vector DBs (FAISS, Pinecone, Chroma).

3. Agentic AI Development

• Lead design and development of

  Agentic AI frameworks

  for autonomous workflows.
• Implement multi-agent systems with memory, planning, tools, and coordination.
  
• Integrate AI agents with external APIs, knowledge bases, and enterprise systems.
  

4. MLOps & Deployment

• Deploy ML/LLM models using

  Docker, Kubernetes, CI/CD, TorchServe, MLflow

  .
• Optimize inference cost, latency, and throughput at scale.
  
• Build monitoring dashboards for AI/ML reliability and performance.
  

5. Leadership & Strategy

• Provide technical leadership to AI/ML engineers and data scientists.
  
• Collaborate with product, cloud, and data engineering teams to align architecture with business goals.
  
• Conduct code reviews, enforce best practices, and maintain high engineering standards.
  
• Drive AI innovation roadmap and emerging tech adoption (Agentic AI, LLMOps, etc.).
  

Required Qualifications

Technical Must-Haves

• 10+ years

  in AI/ML engineering and solution architecture.
• Strong

  Python programming

  with production-level coding.
• Hands-on experience with

  GenAI, LLMs, and Agentic AI frameworks

  (LangChain, CrewAI, AutoGen, LangGraph).
• Deep knowledge of

  PyTorch, TensorFlow, Hugging Face Transformers

  .
• Strong experience in

  RAG pipelines, embeddings, vector DBs

  .
• Proven experience designing

  scalable AI microservices

  using FastAPI/Flask.
• Strong understanding of

  MLOps

  : Docker, Kubernetes, GitHub Actions, MLflow, Sagemaker, Azure ML, GCP Vertex.
• Experience in architecting distributed systems, caching, messaging (Kafka/Redis).
  
• Excellent understanding of LLM evaluation, safety, guardrails, and observability.
  

Soft Skills

• Strong architectural thinking and problem-solving mindset.
  
• Ability to mentor engineering teams and conduct technical reviews.
  
• Excellent communication and stakeholder management.
  
• Proven ability to translate business needs into AI/ML solutions.
  

Preferred Qualifications

• Experience in building

  enterprise GenAI platforms

  or

  AI agent ecosystems

  .
• Experience in

  LLM fine-tuning / quantization / optimization

  .
• Experience with

  RLHF, LORA

  , or custom training pipelines.
• Knowledge of

  Responsible AI, governance, safety, auditability

  .
• Prior experience in

  healthcare, BFSI, retail, or enterprise AI

  .