Senior Manager, AI & Analytics Engineering

Coca-Cola; Georgia; In loco; USD 150.000 - 210.000; Tempo pieno

Descrizione del lavoro

Coca‑Cola seeks a Senior Manager, AI & Analytics Engineering to lead hands‑on development of AI-driven analytics products for over 1,000 users across North America. You will partner with analytics, data science, engineering, and product teams to design practical AI solutions and drive adoption.

You will build agents and services end‑to‑end, balance speed and value with governance, security, and scalable deployment, and mentor engineers in a fast‑moving enterprise environment.

Competenze

• Eight+ 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 e

Mansioni

• Build AI and analytics solutions that create value.
• Partner with business leaders to identify and prioritize high‑value use cases, then translate ambiguous questions into clear solution designs and production capabilities.
• Personally build AI agents, analytical applications, and supporting services; own delivery from framing to adoption and improvement.
• Choose the right approach for the problem, combining deterministic analytics, ML, retrieval, LLMs, and agentic workflows where appropriate.
• Connect AI with trusted analytics by preserving definitions, metrics, filters, sources, freshness, lineage, and authorization.
• Integrate solutions with Microsoft Fabric, OneLake, Lakehouses, Warehouses, Power BI models, Azure ML, Azure AI Search, and other enterprise capabilities.
• Build secure, parameterized APIs and tools that connect agents to governed data, analytical methods, applications, and business workflows.
• Strengthen reusable engineering foundations with patterns for model access, orchestration, retrieval, context and memory, tool execution, and automation.
• Engineer production Python and API services with automated testing, CI/CD, monitoring, and clear ownership; design for performance and cost efficiency.
• Evaluate frameworks and managed services via prototypes and pragmatic architecture without unnecessary complexity or lock‑in.
• Ensure quality, governance, and continuous improvement with regression capabilities and production telemetry.
• Implement secure identity, authorization, guardrails, audit logging, and responsible AI controls in partnership with Architecture, Security, Privacy, and Data Governance teams.
• Provide hands‑on technical leadership by contributing code, guiding design decisions, and mentoring engineers.
• Explain technical choices and risks clearly to business and technical stakeholders, and help teams turn AI capabilities into usable products.

Conoscenze

Python

SQL

APIs

Cloud design

System design

Formazione

Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, Engineering, Analytics, or related field

Strumenti

Azure Machine Learning

Azure AI Search

Power BI

Microsoft Fabric

Descrizione del lavoro

Job Description Summary:

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 e