Principal AI Engineer
Posted Updated
Description
At BMC, we don’t do ordinary. Across BMC, our AI teams are building the next generation of agentic AI, capabilities that help enterprises run their most critical systems faster, more resiliently, and with confidence. Our agentic AI empowers autonomous agents to detect, diagnose, and help resolve complex problems with speed, accountability, and human oversight.
We’re not hiring for roles; we’re hiring for impact. You’ll solve hard problems, build smart solutions, and work with people who challenge and champion you, and see your work make a difference for customers running mission-critical systems.
You will build autonomous and semi-autonomous AI agents that plan, reason, make decisions, and execute tasks, and orchestrate multi-agent systems that integrate with tools, APIs, and enterprise applications. Everything you build must meet enterprise standards: secure, reliable, scalable, observable, and governed.
Here is how, through this role, you will contribute to BMC’s and your own success:
- Design, develop, and maintain enterprise-grade agentic AI applications using modern LLMs and AI frameworks.
- Build autonomous and semi-autonomous AI agents capable of planning, reasoning, decision-making, and task execution.
- Develop multi-agent systems and orchestrate interactions between agents, tools, APIs, and enterprise applications.
- Integrate AI agents with internal and external services through APIs, databases, messaging systems, and workflow engines.
- Build and deploy RAG solutions using vector databases and enterprise knowledge sources.
- Optimize AI workflows for performance, scalability, reliability, and cost efficiency.
- Implement observability, monitoring, guardrails, and governance mechanisms for production AI systems.
- Build and run agent evaluations — define eval sets, test behaviour, iterate on prompts, tools, and workflows.
- Package and deploy solutions in production; apply strong diagnostics across the AI stack.
- Collaborate with product, architects, and stakeholders; contribute to reviews, documentation, and best practices.
- Set applied AI architecture and investment priorities across products.
- Make consequential tradeoffs among models, platforms, build-vs-buy, and risk controls.
- Drive org-wide adoption of AI capabilities with measurable outcomes and managed risk.
- Align product, platform, security, and quality leaders on a coherent AI direction.
To ensure you’re set up for success, you will bring the following skillset & experience:
- Typically, 12+ years of relevant experience
- Bachelor's or Master's in Computer Science, AI, ML, or related field (or equivalent experience).
- Hands-on experience with LLMs (OpenAI, Anthropic, Gemini, Llama, or equivalent)
- Familiarity with agentic frameworks (LangGraph, LangChain, CrewAI, or similar) and agentic SDKs as level requires.
- Understanding of prompt engineering, tool/function calling, planning, reasoning, and workflow orchestration.
- Python; REST APIs, microservices, cloud, containers, CI/CD, and testing — depth scales with level.
- Evals for LLM/RAG/Agent use cases; observability (Langfuse, OpenSearch, OpenTelemetry) as level requires.
- Strong communication and ownership mindset.
- Evidence of setting AI architecture/priorities across multiple products.
- Org/BU adoption with measurable outcomes and explicit risk management.
- Sustained influence on where the organization invests in applied AI.
Whilst these are nice to have, our team can help you develop in the following skills:
- Frontier/foundation models beyond API usage (evaluation, adaptation, fine-tuning).
- Open-weight models and local inference (vLLM, Ollama, Hugging Face).
- Data exploration techniques and tools.
- Agile / Atlassian; advanced IDEs (Cursor, IntelliJ, VSCode).