We are looking for a Principal AI Engineer to build our next-generation Agentic AI platform from 0-1. This is a hands-on, delivery-driven role for a senior engineer who has shipped AI-powered products into real enterprise environments, understands the trade-offs of production AI systems, and takes full ownership of outcomes - not just models or demos. You will work alongside our VP of Engineering, VP of AI and product leadership to define and build the core AI systems of the platform. You will spend most of your time designing, coding, and shipping production-grade AI capabilities used by external B2B customers. Success is measured by reliability, controllability, cost-efficiency, and customer impact - not novelty. Here is how, through this exciting role, YOU will contribute to BMC's and your own success: 37Design, build, and evolve agentic AI systems that reason, plan, execute, and adapt in production environments. 37Take AI-driven features from concept to production in a true 0-1 product environment. 37Write and review high-quality production code (Python-first) across AI pipelines, inference services, orchestration layers, and supporting systems. 37Implement prompt engineering, tool use, memory, evaluation, and guardrails as first-class engineering concerns, not experiments. 37Design agent frameworks that balance autonomy with determinism, observability, and safety. 37Make pragmatic architectural trade-offs across latency, cost, accuracy, scalability, and maintainability. 37Integrate and operate LLMs (commercial and/or open-source) including model selection, fine-tuning strategies, embeddings, retrieval (RAG), and inference optimization. 37Address real-world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust. 37Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer-specific deployments. 37Design scalable inference and orchestration architectures using containers, APIs, and distributed systems. 37Ensure the platform is shippable, debuggable, and supportable - not fragile or research-grade. 37Act with founder-level ownership: identify gaps, propose solutions, and move forward without waiting for perfect requirements. To ensure you're set up for success, you will bring the following skillset & experience: 3710+ years of professional software development experience, with significant time shipping B2B products used by external customers. 37Strong software engineering foundation with expert-level Python and experience designing production systems. 37Proven experience building, deploying, and operating AI-powered products in production, not just prototypes or research. 37Hands-on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems). 37Deep understanding of at least several of the following: 37Agent frameworks and orchestration 37Prompt engineering and tool-use patterns 37RAG architectures and vector search 37Model evaluation, feedback loops, and monitoring 37Safety, guardrails, and enterprise controls 37Hands-on experience with multiple of the following in real systems: 37LangGraph and/or LangChain 37LlamaIndex 37Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus) 37Prompt engineering as a managed, versioned, testable artifact 37Experience deploying and operating LLMs using: 37AWS SageMaker, Vertex AI, or equivalent managed platforms 37Direct API integrations (OpenAI, Anthropic) 37Experience designing multi-agent systems or complex agent workflows. 37Experience commercializing AI features under enterprise constraints (security, compliance, uptime). 37Comfort operating in ambiguity and making decisions with incomplete information. Whilst these are nice to have, our team can help you develop in the following skills: 37Contributions to open-source GenAI tooling or internal frameworks used at scale 37Experience with Supervised fine-tuning, Parameter-efficient tuning methods (LoRA, QLoRA), reinforcement learning (RLHF) and preference optimization (PPO, DPO, GRPO). 37Experience deploying LLMs at scale (Kubernetes, model serving, GPU optimization). The annual base salary range represents the low and high end of the BMC salary range for this position. Actual salaries depend on a wide range of factors that are considered in making compensation decisions, including but not limited to skill sets; experience and training, licensure, and certifications; and other business and organizational needs. The range listed is just one component of BMC's employee compensation package. Other rewards may include a variable plan and country specific benefits. At BMC, it is not typical for an individual to be hired at /near the top of the range. A reasonable estimate of the current range is $175,800 - $293,000 Min salary 175,800 Mid point salary 234,400 Max salary 293,000 Min Salary - NEW 175,800 Max Salary - NEW 293,000