Senior AI Engineer
About The Position
BMC is hiring a Senior AI Engineer to build enterprise-grade agentic AI capabilities that help organizations operate critical systems with speed, resilience, and confidence. The role focuses on developing autonomous and semi-autonomous agents that plan, reason, make decisions, and execute tasks, as well as orchestrating multi-agent systems that connect with tools, APIs, and enterprise applications. Solutions must be secure, reliable, scalable, observable, and governed. The engineer will develop and deploy AI applications using modern large language models and frameworks, integrate agents with services and enterprise knowledge sources, and build retrieval-augmented generation solutions. The role also involves optimizing performance, reliability, scalability, and cost; establishing evaluation and production practices; and collaborating across product, architecture, quality, SRE, and security teams. The engineer will help set technical direction and shared standards across teams, lead architecture for cross-product AI capabilities, and coach other AI engineers. The position asks for 5–8 years of relevant experience and a bachelor's or master's degree in computer science, AI, ML, or a related field, or equivalent experience. The stated annual base salary range is $132,975–$221,625. Occasional business travel may be required. The job is listed for Santa Clara, California, with additional locations in Detroit, Houston, San Francisco, New York, and Washington, DC.
Requirements
- 5–8 years of relevant experience.
- Bachelor's or master's degree in Computer Science, AI, ML, or a related field, or equivalent experience.
- Hands-on experience with LLMs such as OpenAI, Anthropic, Gemini, Llama, or equivalent.
- Familiarity with agentic frameworks such as LangGraph, LangChain, CrewAI, or similar, and agentic SDKs appropriate to the level.
- Understanding of prompt engineering, tool/function calling, planning, reasoning, and workflow orchestration.
- Python experience.
- Experience with REST APIs, microservices, cloud, containers, CI/CD, and testing, with depth appropriate to the level.
- Experience with evaluations for LLM, RAG, or agent use cases.
- Experience with observability tools such as Langfuse, OpenSearch, or OpenTelemetry, as appropriate to the level.
- Strong communication and ownership mindset.
- Ability to set technical direction for an AI area, rather than only deliver features.
- Ability to balance innovation speed with enterprise quality, safety, and cost.
- Ability to support multi-team adoption of patterns or practices led.
Nice To Haves
- Experience with frontier or foundation models beyond API usage, including evaluation, adaptation, or fine-tuning.
- Experience with open-weight models and local inference, such as vLLM, Ollama, or Hugging Face.
- Data exploration techniques and tools.
- Agile or Atlassian experience.
- Experience with advanced IDEs such as Cursor, IntelliJ, or VSCode.
Responsibilities
- 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 among 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 evaluation sets, test behavior, and iterate on prompts, tools, and workflows.
- Package and deploy solutions in production and apply diagnostics across the AI stack.
- Collaborate with product teams, architects, and stakeholders; contribute to reviews, documentation, and best practices.
- Define shared agentic patterns, evaluation practices, and production standards across teams.
- Lead architecture for cross-cutting AI capabilities used by multiple products or squads.
- Coach AI engineers and raise standards for quality, safety, and cost discipline.
- Partner with AI Quality, SRE, and Security on release gates and operational practices.
- Set technical direction for an AI area and support multi-team adoption of patterns or practices.
Benefits
- Country-specific benefits may be available.
- A variable compensation plan may be available.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
