Lead GenAI Cloud Developer

Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI. What is The Role At Elastic, we don’t just connect dots; we power the future of work. As the creators of Elasticsearch, Kibana, and Logstash, we are uniquely positioned to turn vast data into actionable intelligence. We are looking for a visionary Lead GenAI Cloud Developer to spearhead the evolution of ElasticGPT—transforming it from a conversational assistant into a proactive, task-oriented agentic ecosystem that removes friction from every Elastician's day. Your Mission: From Chat to Action You will lead the IT GenAI roadmap, moving beyond simple chat interfaces to build intuitive, action-oriented agentic workflows. Your goal is to mature our internal GPT tool’s (ElasticGPT) capabilities so it doesn't just answer questions—it executes tasks, automates complex processes, and serves as the primary driver of workforce productivity. This role requires experience building and operating real-world GenAI systems under constraints of scale, cost, latency, and enterprise governance—not just prototype or experimental workflows. What You Will Be Doing AI Agent Strategy Development: 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. Production RAG Retrieval: 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. Infrastructure MLOps: Oversee the full model lifecycle—from fine-tuning to deploying scalable APIs on Kubernetes—across major cloud providers (AWS, Azure, GCP). Optimization Observability: Own token efficiency, latency, and cost management. Implement end-to-end tracing (OpenTelemetry) and evaluation pipelines to measure performance. Multi-Model Orchestration: Design and operate LLM gateway architectures for routing, fallback, spend control, and governance across multiple model providers. Protocol Tool Architecture: 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. Governance Reliability: Design robust agents with retries, scoped permissions, and audit trails to ensure secure, reliable execution in a business tech stack. What You Bring Advanced Agentic Expertise: Proven track record of building production-grade agents and RAG systems using frameworks like LangGraph/LangSmith. Technical Proficiency: Mastery of Python and TypeScript; deep experience with PyTorch, TensorFlow, or Hugging Face. Elasticsearch Mastery: Deep knowledge of ESRE, vector indexing (HNSW), and relevance tuning. System Design Intuition: Strong ability to balance the trade-offs between latency, cost, and response quality across multi-model environments (OpenAI, Anthropic, Vertex AI). Cloud Scaling: Extensive experience with Kubernetes and managing high-concurrency cloud infrastructure. Strategic Ownership: A "builder" mindset with the ability to translate complex business needs into technical roadmaps and influence senior stakeholders. Education Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or a related field. P ... (truncated, view full listing at source)