Senior AI Data Architect / Solution Engineer - US/Remote Based

BMC Software empowers nearly 80% of the Forbes Global 100 to accelerate business value through their industry-leading portfolio. They are seeking a Senior AI Data Architect / Solution Engineer to design scalable data architectures and build AI products that meet business needs.

Responsibilities:

• Design the reference architecture and delivery patterns that take governed data from the semantic layer to every downstream consumer — dashboards, applications, and AI agents — reliably and at scale, with the build-vs-buy calls and standards that let the platform grow without painful rework
• Architect and build the Data Agent, the natural-language interface that lets business users query governed data without writing SQL; design agentic workflows with orchestration, tool/function calling, retrieval, evaluation, and guardrails
• Co-design the semantic layer (HoneyDew / DBT) — the metrics, dimensions, and governed business meaning that power both analytics and AI — ensuring agents query governed definitions, not raw tables
• Serve as technical lead and trusted advisor; set patterns, run design reviews, and mentor engineers, partnering with the Data Engineer to take designs to production
• Translate ambiguous business problems into clear technical requirements; own stakeholder relationships and move fluently between executive framing and engineering detail
• Take features and subsystems end-to-end, proactively identifying gaps and driving work forward even when requirements are incomplete or evolving

Requirements:

• 8+ years across data architecture and hands-on engineering, including production systems that held up under real enterprise use
• Strong proficiency in Python and SQL, with deep, hands-on experience designing and building on Snowflake and DBT
• Proven experience designing and building LLM / agentic solutions — RAG, function / tool calling, orchestration (LangChain / LlamaIndex / LangGraph), prompt engineering, and evaluation / observability
• Deep understanding of the semantic layer as the architectural bridge between source data and business meaning — and why it is foundational to trustworthy AI
• Demonstrated ability to translate complex technical concepts for non-technical stakeholders and influence architecture decisions across teams
• Strong communication and the ability to lead through execution in a fast-moving environment
• Cloud-native architecture across AWS, Azure, or GCP
• Vector databases, embeddings, and retrieval evaluation
• CI/CD and modern engineering practices for data and AI systems
• Containerization and distributed systems (Docker, Kubernetes, microservices)
• HoneyDew or other universal/headless semantic layers (HoneyDew, Cube, AtScale, dbt Semantic Layer)
• Snowflake Cortex / Snowflake Intelligence, or production text-to-SQL systems
• Prior technical-lead or principal-engineer role with mentorship responsibility
• Background bridging pre-sales / solution engineering and hands-on delivery