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Senior AI Engineer
The Coca-Cola Company. Design, build, and deploy production-grade AI solutions across Coca-Cola's digital product portfolio .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying AI solutions, with strong proficiency in Python and experience in MLOps practices. Capable of integrating AI capabilities into enterprise applications while ensuring compliance with responsible AI practices.
Highest-signal resume keywords
Python ProficiencyMLOps PracticesAzure Cloud DeploymentAI Model OptimizationAgile Delivery Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI EngineeringMachine Learning EngineeringRESTful API DevelopmentModel DeploymentVersion ControlCI/CDDockerKubernetesVector DatabasesSemantic Search
Soft Skills
Analytical SkillsProblem-Solving SkillsCommunication Skills
Tools & Technologies
LangChainLangGraphCrewAISemantic KernelMCPA2AAzureAWSGCPGraphRAG
Industry Keywords
AI SolutionsAI Safety ControlsResponsible AI PracticesAI ObservabilityTelemetryEnterprise ApplicationsDigital TwinsIntelligent SimulationsDecision-Support WorkflowsAgile Methodologies
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesPythonC++
About the role
Key responsibilities & impact- Design, build, and deploy production-grade AI solutions across Coca-Cola's digital product portfolio
- Prototype, iterate, and take domain-specific AI agents and GenAI solutions to production
- Implement AI agents using MCP and A2A standards to connect agents with enterprise data, tools, and external systems
- Write, test, maintain, and optimize production-grade AI code in Python and other relevant languages
- Optimize AI models and inference pipelines for performance, reliability, and cost efficiency at scale
- Deploy, monitor, and optimize AI agents and models on Azure cloud infrastructure
- Build and maintain MLOps pipelines for model training, versioning, inference, and CI/CD
- Integrate AI capabilities into enterprise platforms, APIs, product workflows, and user-facing features
- Implement runtime observability and telemetry for AI agents and LLM applications
- Design agent evaluation pipelines, harnesses, benchmark datasets, regression tests, and automated quality scoring
- Engineer retrieval pipelines using semantic layers, knowledge graphs, vector search, and business ontologies
- Implement AI safety controls, policy enforcement, human-in-the-loop workflows, autonomy thresholds, prompt injection defenses, and secure tool execution
- Design and orchestrate multi-agent systems across complex enterprise workflows
- Manage prompt versioning, evaluation, experimentation, routing strategies, cost optimization, and production lifecycle for LLM- and agent-powered applications
- Develop AI capabilities supporting enterprise digital twins, intelligent simulations, predictions, and decision-support workflows
- Collaborate with Technical Leads, software engineers, data engineers, and AI Engineers in agile squads
- Participate in sprint planning, backlog refinement, technical design discussions, code reviews, testing, and collaborative problem-solving
- Maintain technical currency, conduct testing and validation, and contribute to knowledge sharing and engineering best practices
Requirements
What you’ll need- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, Software Engineering, or a related technical field
- 3 to 5+ years of hands-on experience in AI or ML engineering, with a demonstrated track record of taking AI models and solutions from development to production
- Strong proficiency in Python; working knowledge of Java or C++
- Experience building and deploying LLM-powered and agentic AI applications in production using LangChain, LangGraph, CrewAI, Semantic Kernel, MCP, A2A, or similar
- Experience deploying and operating AI and ML solutions on cloud infrastructure; Azure strongly preferred, AWS or GCP acceptable
- Experience with vector databases, embeddings, RAG, GraphRAG, semantic search, or context engineering
- Experience developing RESTful APIs or integrating AI capabilities into enterprise applications
- Experience with MLOps, LLMOps, or AgentOps practices
- Strong software engineering fundamentals, including API design, testing, version control, CI/CD, Docker, Kubernetes, and model deployment and monitoring
- Experience working in Agile delivery environments, including sprint execution, code reviews, and cross-squad collaboration
- Experience implementing responsible AI practices, runtime guardrails, AI safety controls, model explainability, data privacy, and secure agent execution
- Experience implementing AI observability and telemetry, including tracing, reasoning diagnostics, token consumption monitoring, latency, cost, output quality, and runtime performance
- Strong analytical and problem-solving skills
- Excellent communication skills for explaining AI products and trade-offs to technical and non-technical stakeholders
- Currently authorized to work full-time in the United States
- Must not require The Coca-Cola Company's employment sponsorship, including H-1B or other employment-based nonimmigrant visa sponsorship
Benefits
Comp & perks- Annual incentive reference value of 15%
- Full range of medical, financial, and/or other benefits, dependent on the position
- Inclusive culture
- Continuous learning and professional development through internal knowledge sharing and engineering best practices