Lead Genai Cloud Developer

Elastic Elastic · Enterprise · United States · IT

Lead GenAI Cloud Developer responsible for evolving ElasticGPT from a conversational assistant to a proactive, task-oriented agentic ecosystem. This role involves designing and building agentic workflows, production RAG systems, deploying scalable APIs, and managing multi-model orchestration with a focus on scale, cost, latency, and enterprise governance.

What you'd actually do

  1. Lead the evolution of ElasticGPT into a task-oriented agent. Design sophisticated workflows (both agentic and deterministic) using LangGraph/LangChain/Elastic Agent Builder/n8n (or equivalent frameworks) to automate enterprise-wide productivity.
  2. Architect hybrid retrieval systems (BM25, vector search, RRF) using ESRE and Jina AI. Build data ingestion pipelines across Confluence, Jira, GitHub, and ServiceNow to improve answer quality.
  3. Oversee the full model lifecycle—from fine-tuning to deploying scalable APIs on Kubernetes—across major cloud providers (AWS, Azure, GCP).
  4. Own token efficiency, latency, and cost management. Implement end-to-end tracing (OpenTelemetry) and evaluation pipelines to measure performance.
  5. Define multi-agent patterns, state management, tool contracts, and handoff logic for enterprise workflows. Demonstrate working knowledge of MCPs and modern agent tool-discovery patterns.

Skills

Required

  • Python
  • TypeScript
  • PyTorch
  • TensorFlow
  • Hugging Face
  • Kubernetes
  • AWS
  • Azure
  • GCP
  • LangGraph
  • LangSmith
  • Elasticsearch
  • ESRE
  • vector indexing
  • HNSW
  • relevance tuning
  • OpenTelemetry
  • OpenAI
  • Anthropic
  • Vertex AI

Nice to have

  • n8n
  • Jina AI
  • BM25
  • RRF
  • Confluence
  • Jira
  • GitHub
  • ServiceNow
  • Docker
  • MCPs

What the JD emphasized

  • production-grade agents
  • production RAG systems
  • scale, cost, latency, and enterprise governance
  • scalable APIs on Kubernetes
  • multi-model environments
  • high-concurrency cloud infrastructure

Other signals

  • building and operating real-world GenAI systems
  • production-grade agents and RAG systems
  • scalable APIs on Kubernetes
  • multi-model orchestration
  • governance and reliability