Lead the implementation of scalable, distributed computing solutions designed to serve Agentic AI and ensure seamless integration with the Red Hat product portfolio
Define and implement Multi-Agent System (MAS) architectures, including orchestration layers, state machines, tool registries, and resilient routing policies with safe fallbacks
Hands-on experience implementing Model Context Protocol (MCP) for standardized tool/data access and Agent-to-Agent (A2A) or ACP protocols for cross-platform agent communication and task delegation
Contribute to and influence upstream AI/ML communities to steer the evolution of open standards for agentic workflows
Partner with AI/ML vendors and internal teams to refine AI strategies, addressing specific use cases that drive value through Red Hat’s next-generation UX
Develop technical blueprints and multi-product demos that showcase the "Art of the Possible" using the Red Hat AI stack
Proactively explore emerging AI technologies to identify opportunities for incorporating new capabilities into software development workflows and tooling
Drive AI integration within the software development lifecycle (SDLC), sharing successful experiment use cases with stakeholders to foster broader innovation

Requirements

7+ years of relevant software engineering experience
Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience
Proven experience building agents and tooling frameworks; deep expertise in LangGraph, PydanticAI, or similar state-management libraries
Experience implementing sophisticated RAG, long-term memory systems, semantic caches, and vector databases
Expert-level proficiency in Python or Go, with a specific focus on building resilient, asynchronous distributed systems
Solid experience with containers and orchestration via OpenShift or Kubernetes
Familiarity with model parallelization, quantization, and memory optimization (e.g., vLLM, DeepSpeed, OpenVino)
Experience with GitOps, automation pipelines, and managing the AI/ML lifecycle in production environments
Direct experience with Agent Evaluation (Eval) frameworks (measuring success rates/hallucinations) and implementing Guardrails & Governance (preventing prompt injection/infinite loops)
A history of open-source contributions or active participation in the AI/ML community (GitHub, Research, or Upstream)

Benefits

Comprehensive medical, dental, and vision coverage
Flexible Spending Account - healthcare and dependent care
Health Savings Account - high deductible medical plan
Retirement 401(k) with employer match
Paid time off and holidays
Paid parental leave plans for all new parents
Leave benefits including disability, paid family medical leave, and paid military leave
Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonGoMulti-Agent System (MAS) architecturesModel Context Protocol (MCP)Agent-to-Agent (A2A) protocolsRAG systemslong-term memory systemssemantic cachesvector databasesstate-management libraries

Soft Skills

leadershipcollaborationcommunicationinnovationproblem-solving

Certifications

Bachelor’s degree in Computer Science