Principal AI Engineer - Office of the CTO
About The Position
BMC empowers nearly 80% of the Forbes Global 100 to accelerate business value, faster than humanly possible. Our industry-leading portfolio unlocks human and machine potential to drive business growth, innovation, and sustainable success. BMC does this in a simple and optimized way by connecting people, systems, and data that power the world’s largest organizations so they can seize a competitive advantage. We're looking for a Principal AI Engineer to architect, build, and harden the agentic AI systems that power our products. This is a hands-on, full-stack AI role: you'll work across the entire AI stack from the foundation (model lifecycle, experimentation, infrastructure) through shared services (agent orchestration, RAG and grounding, gateways and routing) up to the agents and workflows that reach customers. You'll set the technical direction for how we design agents, prove what works through rigorous evaluation, and turn promising prototypes into reliable, observable, governed production systems.
Requirements
- 8+ years of building and operating scalable, production-grade software, with significant recent depth in AI/ML.
- Proven experience designing, building, and shipping agentic AI systems (autonomous/multi-step agentic workflows, multi-agent frameworks, and Generative AI copilots) in production.
- Hands-on experience building evaluation and experimentation frameworks for LLM/agentic systems (offline + online evals, LLM-as-a-judge, benchmarking, CI/CD gates, production monitoring).
- Strong agentic engineering skills, with modern agent/LLM frameworks (e.g., LangGraph, Google ADK, or comparable).
- Experience with RAG and retrieval (vector stores, grounding, citations) and familiarity with AgentOps/LLMOps and production monitoring.
- Solid software engineering fundamentals with productization tooling: Git, Docker, Kubernetes, CI/CD, and cloud (AWS / Azure / GCP).
- The judgment to operate in fast-moving, ambiguous environments and the communication skills to lead without authority.
Nice To Haves
- Experience with model customization (SFT, RLHF, DPO/GRPO), eval/observability platforms and bridging applied research and engineering is a nice to have.
Responsibilities
- Own the architecture for agentic systems end to end - reasoning and planning, tool/function calling, multi-agent coordination, routing, memory, and human-in-the-loop handoffs and define reusable patterns and reference implementations.
- Prototype new agentic capabilities quickly, then drive the ones that prove into production-grade systems; write production-quality code and set the engineering bar.
- Build and integrate across the stack: RAG and knowledge services, model/tool routing, prompt and context management, orchestration runtimes, and inference serving. Evaluate and adopt emerging models, frameworks, and protocols.
- Stand up the evaluation and experimentation strategy that gates what we ship, offline suites and golden datasets, regression tests in CI/CD, online evaluators on production traffic, calibrated LLM-as-a-judge graders, and A/B experiments to ensure safe and reliable deployments of agents in production.
- Define the metrics that matter (task success, grounded-ness, cost, safety, etc.) and define and build the tracing and observability to measure them across multi-turn interactions, closing the loop from error analysis to continuous improvement.
- Set technical direction across teams, mentor engineers, and translate complex architectures into clear guidance for partners and customers.
Benefits
- variable plan
- country specific benefits
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What This Job Offers
Job Type
Full-time
Career Level
Principal
Education Level
No Education Listed
