Director of Software Engineering Sales & Controls Technology

• Leads technology and process implementations to achieve functional technology objectives
• Accountable for decisions that influence teams’ resources, budget, tactical operations, and the execution and implementation of processes and procedures
• Sets direction and governance for agentic AI-enabled engineering and SDLC/ADLC automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives at scale.
• Carries governance accountability for coding decisions, control obligations, and measures of success such as cost of ownership, maintainability, and portfolio operations
• Delivers technical solutions that can be leveraged across multiple businesses and domains
• Leadership capabilities: leads/mentors technologists, influences stakeholders, and supports hiring/screening for deep technical roles. 

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise 
• Expert Python engineer: builds production-grade services/libraries with strong testing, performance, and reliability practices.
• Proven AI/agentic workflow architecture experience (tool-use patterns, orchestration, context management, guardrails, human-in-the-loop).
• Strong solution/enterprise architecture skills: designs end-to-end systems and documents patterns, integrations, and data flows clearly.
• Hands-on AWS experience delivering secure, scalable cloud-native services (IAM/security, networking, compute, monitoring/observability).
• VectorDB + retrieval (RAG) experience: embedding/indexing pipelines, similarity search, metadata filtering, and relevance/latency tradeoffs.
• Experience building personalization driven by user profiles/entitlements/preferences and applying it to AI assistant experiences.
• Familiarity with voice + chat conversation data processing pipelines to enable assistant insights and personalization.
• Platform ownership mindset for large user bases (~3,000 users): availability, resiliency, incident response, and safe release practices.

• Strong responsible AI / controls understanding: secure handling of sensitive inputs/outputs, governance, and quality gates using enterprise-authorized tools.