Senior Manager, AI & Analytics Engineering

Coca-Cola•Atlanta, GA

About The Position

The Senior Manager, AI & Analytics Engineering is a hands-on technical leader who translates high-value business and analytical problems into production AI agents and analytical solutions. The team is building capabilities for more than 1,000 users across Commercial, Strategy, Customer, Marketing, Category Advisory, and Executive Leadership. The role partners with these groups and with analytics, data science, engineering, and product teams to identify opportunities, design practical approaches, build capabilities, and drive adoption across North America. The position combines applied AI, analytics engineering, and system design. It involves building agents and analytical applications while developing reusable services and patterns for governed data access, evaluation, security, observability, and deployment. The role balances delivery speed and business value with analytical integrity, reliability, scale, and cost. As a player-coach, the Senior Manager contributes production code, leads system-design decisions, reviews code and architecture, mentors engineers, and establishes practical standards. The role also communicates technical choices, risks, and trade-offs to business and technical stakeholders, helping teams turn emerging AI capabilities into useful, supportable products.

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.
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.
Must be currently authorized to work in the United States on a full-time basis and must not require The Coca-Cola Company’s sponsorship to continue working legally in the United States.

Nice To Haves

Experience with 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.
Experience with 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.
Experience building and scaling AI or analytical products in a complex enterprise with multiple business domains, sensitive data, and diverse users.

Responsibilities

Partner with business leaders to identify and prioritize high-value use cases, translating ambiguous questions and analytical needs into solution designs, prototypes, and production capabilities used in decisions and workflows.
Personally build AI agents, analytical applications, and supporting services, owning delivery from opportunity framing and architecture through testing, deployment, adoption, and improvement.
Select appropriate approaches for each problem, combining deterministic analytics, machine learning, retrieval, large language models, and agentic workflows where useful.
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, connecting agents to governed data, analytical methods, applications, and business workflows.
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, applying pragmatic architecture that avoids unnecessary complexity and lock-in.
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 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.
Contribute production code, lead system-design decisions, review code and architecture, mentor engineers, and establish practical delivery standards.
Explain technical choices, risks, and trade-offs to business and technical stakeholders and help teams develop useful, supportable AI products.