Generative AI/ML Lead

Role Summary

Generative AI/ML Lead

Key Responsibilities

• Strategize

  GenAI or ML solution architecture

  across cloud, edge, and hybrid environments.
• Experience in evaluating LLM applications and developing observability frameworks
• Research and implement latest technology and frameworks
• Experience working with diffusion models like Stable Diffusion, MidJourney, Runway, Imagen, Veo etc.
• Experience in working with structured data along with LLMs frameworks
• Lead design and implementation of

  RAG-based

  knowledge systems and

  agentic AI workflows

  .
• Select and integrate

  enterprise-grade AI models

  (OpenAI, Anthropic, Mistral, LLaMA, custom fine-tuned models).
• Drive

  cloud-native AI deployments

  leveraging AWS, Azure, and GCP AI services.
• Architect

  LLMOps

  pipelines for scalable AI model lifecycle management.
• Implement

  AI governance, risk management, and compliance frameworks

  for regulated industries.
• Lead

  PoCs and pilots

  , then guide them to production-grade rollouts.
• Evaluate and optimize

  AI cost, performance, and security

  trade-offs.
• Mentor and manage AI engineers, setting

  best practices for RAG, agentic, and edge AI

  . Be responsible and own delivery outcomes for all GenAI initiatives, ensuring scope, timelines, and quality targets are met.
• Collaborate with business stakeholders to

  translate needs into AI-powered products

  .
• Establish

  AI monitoring and observability

  (drift detection, hallucination tracking, usage analytics).

Required Skills

• 8–15 years of technology experience, with

  4+ years in AI/ML

  and

  2+ years in GenAI

  .
• Proven leadership in

  cloud AI architectures

  (AWS Bedrock/SageMaker, Azure OpenAI, GCP Vertex AI).
• Strong expertise in

  RAG architectures, embeddings, and semantic search

  .
• Experience in

  agentic AI frameworks

  (LangGraph Agents, Autogen, CrewAI, OpenAI/Google Agent SDK).
• Advanced knowledge of

  vector stores, distributed search, and multi-modal AI

  .
• Proficiency in

  edge AI deployments

  and low-latency AI inference optimization.
• Expertise in

  cloud networking, identity, and security

  for AI workloads.
• Strong understanding of

  AI product lifecycle

  , from ideation to production.
• Excellent

  stakeholder management

  and

  team leadership

  skills.
• Step in as a

  hands-on problem solver

  to debug, optimize, or redesign solutions when engineers encounter roadblocks.
• Conduct

  code and architecture reviews

  to maintain engineering excellence.

Preferred Skills

• Experience in build, test and deploy various ML models
• Experience in building MCP, A2A protocol
• Experience with

  AI marketplaces and model hosting

  .
• Multi-cloud AI cost optimization strategies.
• Contributions to

  AI architecture standards

  in enterprise settings.