Lead AI/ML Engineer Generative AI Platforms

AI Solution Development

ML Engineering & Pipelines

Infrastructure & MLOps

Stakeholder Collaboration

Best Practices & Documentation

• • Design and implement LLM-powered applications such as:
    • Retrieval-Augmented Generation (RAG) pipelines
    • AI agents and multi-agent systems
    • Chatbots and generative AI integrations
  • Integrate with commercial and open-source LLMs (OpenAI, Anthropic, Llama, Mistral)
  • Build and optimize ML/data pipelines for:
    • Training, fine-tuning, and evaluation
    • Embeddings and vector search workflows
  • Develop scalable and reusable AI/ML components
  • Architect and manage production-grade AI infrastructure
  • Implement:
    • Containerization (Docker, Kubernetes)
    • CI/CD pipelines for ML workflows
    • Model monitoring, logging, and scaling strategies
  • Engage with business stakeholders to:
    • Understand requirements
    • Identify AI use cases
    • Translate ambiguous needs into technical solutions
  • Collaborate with cross-functional teams to align AI solutions with business goals
  • Establish standards for:
    • Prompt engineering
    • Model evaluation
    • Responsible AI practices
  • Document system architecture, workflows, and operational runbooks