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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Lead Software Engineer - AgenticAI - **Company:** BMC Software, Inc. - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $152,925.0 - $254,875.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Python (Programming Language), Uptime, Open Source Technology, Performance Tuning, Search Technologies, Software Engineering, Reinforcement Learning, Graphics Processing Unit (GPU), Google Cloud, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, AI Platforms, Kubernetes, Production Code, Machine Learning Operations, Virtual Agents, Bmc Software, Api Management - **Published:** September 16, 2026 - **Apply:** https://jobs.localjobnetwork.com/apply/add/87158106/1 ## About the Role * 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). ## Description 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 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., BMC Software maintains a strict policy of not requesting any form of payment in exchange for employment opportunities, upholding a fair and ethical hiring process. ## Related Videos - [Beyond Chatbots: How to build Agentic AI systems](https://www.wearedevelopers.com/videos/1629-beyond-chatbots-how-to-build-agentic-ai-systems) - [DevOps at Netflix](https://www.wearedevelopers.com/videos/270-devops-at-netflix) - [Understanding Kubernetes in a visual way](https://www.wearedevelopers.com/videos/100085-understanding-kubernetes-in-a-visual-way) - [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure) - [Leading with Reliability: Applying SRE Principles to Build Stronger Engineering Organizations](https://www.wearedevelopers.com/videos/100185-leading-with-reliability-applying-sre-principles-to-build-stronger-engineering-organizations) - [Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing](https://www.wearedevelopers.com/videos/100333-instant-kai-sandboxes-with-vcluster-multi-tenant-multi-scheduler-gpu-sharing) ## Related Articles - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j)