Senior Consultant - Genai Full Stack Developer

Senior Consultant role focused on designing, building, and deploying end-to-end Generative AI (GenAI) solutions for enterprise clients. This includes RAG, multi-agent orchestration, real-time AI task pipelines, and knowledge graph integration, with a strong emphasis on scalability, security, and governance within regulated environments. The role involves full-stack development (Python/FastAPI, Node.js, React) and operationalization of AI deployments.

What you'd actually do

  1. Lead business and technical requirements elicitation with client stakeholders; own end-to-end gap analysis; translate needs into solution architecture, detailed technical specifications, and delivery-ready backlog artifacts.
  2. Design, build, test, and deploy GenAI application platforms—comprising Python/FastAPI AI microservices, Node.js backend APIs, and React frontends—using asynchronous task orchestration (Redis pub/sub, Server-Sent Events) to deliver real-time AI workflows at enterprise scale; ensure non-functional requirements (security, performance, reliability, observability) are met.
  3. Own end-to-end retrieval-augmented generation (RAG) implementations (ingestion, chunking, embedding, indexing, retrieval, orchestration); define prompt engineering standards and evaluation harnesses to measure quality and reduce hallucinations.
  4. Architect agentic AI workflows using LangChain and LangGraph (tool-using agents, multi-step orchestration, parallel multi-agent patterns); integrate LLM pipelines with knowledge graphs (Neo4j) for structured reasoning over audit and compliance data; implement human-in-the-loop checkpoints, auditability controls, and enterprise governance guardrails.
  5. Evaluate and integrate frontier LLMs (Gemini 2.5 Pro/Flash, Claude, GPT-4o) and specialized models; define LLM selection criteria, cost/latency tradeoffs, and quality benchmarks; run prompt iteration cycles and structured output evaluation to meet acceptance criteria across audit-specific use cases.

Skills

Required

  • GenAI solutions development
  • Retrieval-Augmented Generation (RAG)
  • multi-agent orchestration
  • LangChain
  • LangGraph
  • Python/FastAPI
  • Node.js
  • React
  • knowledge graph integration (Neo4j)
  • prompt engineering
  • evaluation harnesses
  • API and integration service design
  • RESTful interfaces
  • streaming endpoints
  • containerized deployments (Docker, Kubernetes, Helm)
  • monitoring and observability for AI workloads
  • responsible AI adoption

Nice to have

  • LLM selection criteria
  • cost/latency tradeoffs
  • quality benchmarks
  • document parsing
  • structured extraction
  • embedding preparation
  • data governance
  • risk teams
  • lineage
  • access controls
  • data quality standards

What the JD emphasized

  • enterprise governance expectations
  • enterprise scale
  • enterprise governance guardrails
  • regulated environments

Other signals

  • design and deliver end-to-end Generative AI (GenAI) solutions
  • Retrieval-Augmented Generation (RAG) multi-agent orchestration
  • real-time AI task pipelines
  • knowledge graph–powered reasoning
  • enterprise governance expectations