Senior AI Automation & Integration Engineer

Description JOB OVERVIEW The Senior AI Automation & Integration Engineer leads the technical execution of the AI Center of Excellence’s automation, integration, and enablement capability. Working within the direction established through BS&A’s AI engineering standards and governance model, this role defines the implementation patterns, reference components, and reliability standards that other teams build on, and is accountable for those decisions holding up as the AI program scales.

This is an integration, automation, and enablement role, not a model-research, model-training, or GPU-infrastructure role. The focus is internal. AI in our products is owned elsewhere. The work is deciding how approved models connect to BS&A’s systems and data, defining the reusable patterns other teams build on, and owning the reliability and cost of those decisions in production.

Where the AI Automation & Integration Engineer configures and integrates within established patterns, this role defines them: how standards become working capabilities, which reference architectures are practical, what belongs in the shared foundation, how evaluation and observability are implemented, and whether a proposed solution conforms to standard. This is a hands-on technical role rather than a people-management role, though it mentors and sets the technical bar for the rest of the capability.

Requirements KEY RESPONSIBILITIES Technical Leadership For Shared AI Capabilities

• Lead the implementation architecture for BS&A’s shared AI capabilities, including model APIs and gateways, MCP-based and comparable tool integrations, orchestration workflows, shared frameworks, runtime services, evaluation, monitoring, and cost controls.
• Translate BS&A’s AI engineering standards into practical engineering patterns, working capabilities, reference implementations, and technical guardrails that teams can consistently apply.
• Decide, in partnership with the VP of AI Transformation and CTO, what the CoE builds centrally, what departmental engineering builds on shared capabilities, and what is purchased, configured, or intentionally not pursued.
• Establish and maintain reliability, cost, maintainability, and scalability standards as adoption grows across departments.

Solution Engineering & Delivery

• Default to configuring what BS&A already owns or can buy, and build custom only when nothing available does the job.
• Lead the build of enterprise AI solutions where centralized ownership is appropriate, and design the shared components departmental engineering teams build on top of.
• Own technical decisions on build, buy, and configure, including proof-of-concept work and analysis of cost, quality, latency, security, scalability, and maintainability for the highest-stakes solutions.
• Apply and refine the least-expensive-model-capable-of-the-job principle across the portfolio, balancing quality, cost, latency, security, and maintainability rather than optimizing a single solution.

Production Readiness, Reliability & Cost Ownership

• Own the technical readiness assessment for production reviews, complementing the business evidence compiled by the AI Portfolio Analyst and the data, infrastructure, and security assessments owned by IT & Security.
• Own monitoring, evaluation, and cost-tracking instrumentation standards across all production AI solutions, not only those built centrally.
• Lead incident response for CoE-managed components with IT & Security, and drive root-cause fixes that prevent recurrence.
• Define what a complete handoff looks like so solutions transfer cleanly to the team that will own them.
• Own the technical side of the cost-of-value discipline: keeping operating cost within the cost-of-value band defined in the CoE’s portfolio standards, and flagging solutions at risk of breaching it.

Standards Execution & Governance Partnership

• Lead the technical execution of BS&A’s AI engineering standards, translating architecture, model eligibility, evaluation, and integration requirements into shared components, reference implementations, engineering guidance, and production controls.
• Drive consistent application of those standards across centrally built solutions and departmental engineering teams building on shared capabilities.
• Identify gaps, implementation challenges, and emerging technical needs, and recommend updates to the standards to the VP of AI Transformation, in partnership with t…