Senior Manager, AI & Analytics Engineering

The Senior Manager, AI & Analytics Engineering will be a hands-on technical leader who turns high-value business and analytical problems into production agents and analytical solutions. The team is building for more than 1,000 users across Commercial, Strategy, Customer, Marketing, Category Advisory, and Executive Leadership. This person will partner across those groups and with analytics, data science, engineering, and product teams to identify opportunities, design practical approaches, build working capabilities, and drive adoption across North America.
The role combines applied AI, analytics engineering, and system design. This person will build agents and analytical applications while strengthening the reusable services, governed data access, evaluation, security, observability, and deployment patterns behind them. They will balance speed and business value with analytical integrity, reliability, scale, and cost.
What You'll Do for Us:
Build AI and analytics solutions that create value

• Partner with business leaders to identify and prioritize high-value use cases, then translate ambiguous questions and analytical needs into clear solution designs, working prototypes, and production capabilities used in real decisions and workflows.
• Personally build AI agents, analytical applications, and supporting services; own delivery from opportunity framing and architecture through testing, deployment, adoption, and improvement.
• Choose the right approach for the problem, combining deterministic analytics, machine learning, retrieval, large language models, and agentic workflows where each adds value.

Connect AI with trusted analytics

• Partner with business experts, analysts, data scientists, and data engineers to preserve business definitions, metric grain, filters, source, freshness, lineage, and authorization in AI-enabled analysis.
• Integrate solutions with Microsoft Fabric, OneLake, Lakehouses, Warehouses, Power BI semantic models, Azure Machine Learning, Azure AI Search, and other approved enterprise capabilities.
• Build secure, parameterized APIs and tools, including MCP-compatible services, that connect agents to governed data, analytical methods, applications, and business workflows.

Strengthen the reusable engineering foundation

• Build reusable patterns for model access, orchestration, retrieval, context and memory, tool execution, structured responses, human approvals, workflow recovery, and automation.
• Engineer production Python and API services with automated testing, CI/CD, monitoring, and clear operational ownership; design for performance, concurrent demand, reliability, scale, and cost efficiency.
• Evaluate frameworks and managed services through working prototypes and measurable trade-offs, using pragmatic architecture that avoids unnecessary complexity and lock-in.

Ensure quality, governance, and continuous improvement

• Build evaluation and regression capabilities using benchmark datasets, known-answer tests, adversarial scenarios, release gates, end-to-end tracing, and production telemetry.
• Measure analytical accuracy, task completion, adoption, latency, throughput, reliability, token and compute use, and cost per successful workflow; use the evidence to improve solutions through controlled, versioned releases.
• Implement secure identity, authorization, guardrails, audit logging, human review, and responsible AI controls in partnership with Architecture, Security, Privacy, and Data Governance teams.

Provide hands-on technical leadership

• Operate as a player-coach: contribute production code, lead system-design decisions, review code and architecture, mentor engineers, and establish practical standards that improve delivery across the team.
• Explain technical choices, risks, and trade-offs clearly to business and technical stakeholders, and help teams turn emerging AI capabilities into useful, supportable products.

Qualifications & Requirements:

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering, Analytics, or a related field, or equivalent practical experience.
• Eight or more years of experience across AI, software, data, platform, analytics, or machine-learning engineering, shipping solutions that serve users.
• Recent hands-on experience personally building and shipping AI, analytical, machine-learning, or data products using Python, SQL, APIs, or comparable tools, plus practical exposure to large language models or agentic workflows.
• Strong analytical reasoning and experience with SQL-based data systems, semantic models, Lakehouse architecture, governed data products, or comparable enterprise analytical environments.
• Sound cloud and system-design fundamentals, including testing, asynchronous workloads, security, observability, reliability, deployment, performance, scalability, and cost management.
• Ability to take an ambiguous problem from business need through architecture, implementation, and adoption while influencing engineering, data science, analytics, security, and business teams.

Preferred Experience:

• Microsoft Fabric, OneLake, Power BI semantic models, Azure Machine Learning, Microsoft Foundry/Azure OpenAI, Azure AI Search, and related Azure services.
• Hands-on experience with AI development tools and platforms such as OpenAI Codex, Anthropic Claude, Azure OpenAI, or comparable enterprise-approved capabilities.
• Modern agent frameworks and interoperability patterns such as LangGraph, LangChain, Microsoft Agent Framework, Semantic Kernel, PydanticAI, MCP, or A2A.
• MLOps, LLMOps, or AgentOps practices covering evaluation, experimentation, deployment, observability, governance, and production operations.
• Building and scaling AI or analytical products in a complex enterprise with multiple business domains, sensitive data, and diverse users.