AI/ML Engineer - Agentic

Hewlett Packard Enterprise•San Jose, CA

8d•$136,500 - $276,500•Hybrid

About The Position

The AI/ML Engineer – Agentic is a senior individual contributor responsible for designing, building, and operating a production-grade agentic orchestration platform, including multi-agent workflows and MCP server–based tool infrastructure. The role focuses on enterprise-scale LLM integration, shared retrieval and memory services, and high‑performance backend systems that power agent execution. This position owns reliability, observability, and cloud-native operations for non-deterministic agentic systems in production.

Requirements

Bachelor’s degree in computer science, engineering, information systems, or closely related quantitative discipline.
Typically, 4-7 years’ experience.
Production experience with agentic frameworks: LangGraph (preferred), Claude Agent SDK, or equivalent (not just prototypes)
Deep understanding of multi-agent architectures: supervisor/worker patterns, hierarchical agent graphs, ReAct loops, ReWoo
Hands-on with inter-agent communication protocols: MCP (Model Context Protocol), A2A, tool registry / server registry
LLM API integration at scale: structured outputs, streaming, function/tool calling, error handling
RAG pipeline design and optimization: chunking strategies, re-ranking, hybrid search - Know what knobs to turn for what issues
Vector store experience: OpenSearch or equivalent
Applied ML intuition: fine-tuning concepts, prompt engineering, evaluations, Qlora, PEFT
Backend development: FastAPI, gRPC, Kafka, Redis, message queues, Async System design: Python, API Design GraphQL and/or REST at enterprise scale
Observability and monitoring for non-deterministic systems: LangFuse, Prometheus, or equivalent
Kubernetes: deploying, scaling, and managing workloads (Deployments, Services, ConfigMaps, Secrets)
Container image management: building, tagging, versioning, and pushing images via Docker; familiarity with a container registry (ECR, GCR, Docker Hub)
CI/CD pipelines for automated build and deploy (GitHub Actions, Jenkins, ArgoCD, or similar)
Resource management: CPU/memory limits, autoscaling (HPA/VPA), health probes

Nice To Haves

Master’s desirable.
Multi-tenant architecture awareness: rate limiting, auth, tenant isolation
Knowledge base and cost optimization experience: AWS Bedrock, OpenSearch Serverless

Responsibilities

Design, build, and own a production-grade agentic orchestration platform, implementing scalable multi-agent workflows using frameworks such as LangGraph or equivalent.
Architect, develop, and operate the MCP server infrastructure, including inter-agent communication, tool/server registries, domain isolation, versioning, and lifecycle management.
Integrate and operate LLM services at enterprise scale, supporting streaming, structured outputs, tool/function calling, and robust error handling across agent workflows.
Build and maintain retrieval and memory services for agentic systems, including RAG pipelines, OpenSearch-backed vector stores, hybrid search, and relevance optimization.
Develop and operate high-performance backend services (FastAPI, gRPC, async systems, messaging) that power orchestration, tool execution, and agent runtime behavior.
Own observability and reliability for non-deterministic systems, delivering end-to-end tracing, monitoring, and cost/performance visibility for agent executions.
Manage cloud-native infrastructure and deployment, including Kubernetes workloads, containerized services, CI/CD pipelines, and resource optimization (CPU/memory, autoscaling).