Basic InformationJob Name Lead Software Engineer - AgenticAI
Country United States
State California
City Santa Clara
Date Published 22-Apr-2026
Job ID 46660
Travel You may occasionally be required to travel for business
Additional Locations Milpitas - Mountain View - East Foothills - Los Altos - Stanford - Santa Clara
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HEREDescription and RequirementsWe are looking for a Lead AI Engineer to help build our next generation Agentic AI platform from 0-1. This is a hands on, delivery driven role for an engineer who has shipped AI powered products into real enterprise environments, understands the trade offs of production AI systems, and takes ownership of outcomes, not just models or demos.
You will work alongside our VP of Engineering, VP of AI, senior engineers, 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.
How YOU will contribute to BMC's and your own success- Build and evolve agentic AI systems that reason, plan, execute, and adapt in production environments.
- Lead AI driven features from concept to production in a true 0-1 product environment.
- Write and review high quality production code (Python first) across AI pipelines, inference services, orchestration layers, and supporting systems.
- Implement prompt engineering, tool use, memory, evaluation, and guardrails as first class engineering concerns, not experiments.
- Contribute to the design of agent frameworks that balance autonomy with determinism, observability, and safety.
- Make pragmatic architectural trade offs across latency, cost, accuracy, scalability, and maintainability.
- Integrate and operate LLMs (commercial and/or open source) including model selection, fine tuning strategies, embeddings, retrieval (RAG), and inference optimization.
- Address real world issues: hallucinations, drift, prompt regressions, failure modes, and customer trust.
- Deploy and operate AI services across cloud platforms (AWS, Azure, GCP), including secure enterprise integrations and customer specific deployments.
- Ensure the platform is shippable, debuggable, and supportable - not fragile or research grade.
- Act with strong 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- 6+ years of professional software development experience, with significant time shipping B2B products used by external customers.
- Strong software engineering foundation with expert level Python and experience designing production systems.
- Proven experience building, deploying, and operating AI powered products in production,
- Hands on experience with LLMs and GenAI systems in real applications (e.g., agents, copilots, automation, decision systems).
- Understanding of at least several of the following:
- Agent frameworks and orchestration
- Prompt engineering and tool use patterns
- RAG architectures and vector search
- Model evaluation, feedback loops, and monitoring
- Safety, guardrails, and enterprise controls
- Hands on experience with multiple of the following in real systems:
- LangGraph and/or LangChain
- LlamaIndex
- Vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus)
- Prompt engineering as a managed, versioned, testable artifact
- Experience deploying and operating LLMs using:
- AWS SageMaker, Vertex AI, or equivalent managed platforms
- Direct API integrations (OpenAI, Anthropic)
- Experience designing multi agent systems or complex agent workflows.
- Experience commercializing AI features under enterprise constraints (security, compliance, uptime).
- Comfort operating in ambiguity and making decisions with incomplete information.
Nice to have (our team can help you develop these)- Contributions to open source GenAI tooling or internal frameworks used at scale.
- Experience with supervised fine tuning, parameter efficient tuning methods (LoRA, QLoRA), reinforcement learning (RLHF) and preference optimization (PPO, DPO, GRPO).
- Experience 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 $152,925 - $254,875 
