Manager - Genai Full Stack Developer

Manager role leading teams to deliver end-to-end Generative AI (GenAI) solutions, including RAG and agentic AI, from strategy to adoption. Responsibilities include client discovery, solution design, architecting agentic AI systems, RAG implementation, GenAI model build (training, fine-tuning, validation), deployment, and production operations. Also involves leading development pods and evaluating new frameworks.

What you'd actually do

  1. Lead client discovery, requirements, and solution shaping; translate needs into architecture, technical specifications, delivery plans, and acceptance criteria.
  2. Design, build, and implement custom AI/GenAI solutions tailored to business workflows and risk considerations.
  3. Architect and optimize agentic AI systems (e.g., tool-using agents, multi-step orchestration, multi-agent patterns) and integrate with enterprise platforms.
  4. Lead end-to-end RAG implementations including ingestion, preprocessing, chunking, embeddings, indexing, retrieval, orchestration, and evaluation.
  5. Drive GenAI model build activities (training, fine-tuning, validation), benchmarking, and continuous improvement of quality, safety, latency, and cost.

Skills

Required

  • GenAI / NLP / Agentic AI
  • Python programming
  • Natural Language Processing (NLP)
  • Agentic AI, including LangChain, LangGraph, and LlamaIndex
  • RAG (Retrieval-Augmented Generation)
  • Prompt engineering
  • Vector databases (design/usage/integration)
  • Model build + deployment
  • GenAI model build: training, fine-tuning, validation
  • Model deployment (serving patterns, monitoring, iteration)
  • Containers (e.g., Docker)
  • Data engineering + APIs
  • ETL (extract, transform, load) and data engineering (pipelines, quality, preprocessing)
  • FastAPI (or equivalent) to build backend services
  • API development and integration (RESTful services)
  • Full stack engineering
  • JavaScript/TypeScript
  • HTML/CSS plus SASS/LESS
  • UI/UX design principles
  • Front-end frameworks: React, Angular, or Vue
  • Cloud AI/ML services across Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP)
  • Vertex AI experience

What the JD emphasized

  • GenAI / NLP / Agentic AI
  • Agentic AI, including LangChain, LangGraph, and LlamaIndex
  • RAG (Retrieval-Augmented Generation)
  • GenAI model build: training, fine-tuning, validation
  • Model deployment (serving patterns, monitoring, iteration)

Other signals

  • leading client discovery
  • designing and building custom AI/GenAI solutions
  • architecting and optimizing agentic AI systems
  • leading end-to-end RAG implementations
  • driving GenAI model build activities
  • overseeing model deployment and production operations
  • leading development pods
  • evaluating emerging GenAI/agent frameworks