Generative AI/ML Lead

Role Summary

Generative AI/ML Lead

cloud architecture expertise

RAG and agentic AI patterns

strategic vision

technical depth

edge and hybrid deployments

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.