AI/ML Engineer

Job Description: The AI/ML Engineer will be responsible for designing, developing, deploying, monitoring, and optimizing enterprise AI and machine learning solutions. This includes traditional machine learning models, Generative AI solutions, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, AI agents, and agentic workflows. The role will partner closely with business stakeholders, Data Engineering, Information Security, Risk Management, Compliance, Architecture, and the AI/ML Center of Excellence (CoE) to identify, prioritize, and deliver AI-enabled business capabilities.This role will maintain and enhance AI/ML infrastructure, establish ML Ops and AI Ops practices, implement governance and responsible AI controls, and ensure AI solutions operate securely, reliably, transparently, and in compliance with organizational and regulatory requirements. The AI/ML Engineer will support technologies and platforms such as Microsoft Copilot, Github Copilot, AWS Bedrock, Databricks, Python, machine learning frameworks, model lifecycle tools, AI orchestration frameworks, vector databases, and other emerging AI technologies.The AI/ML Engineer will evaluate emerging technologies and identify opportunities to use AI to improve operational efficiency, decision-making, customer experience, and business outcomes.Education & ExperienceBachelor's degree in Computer Science, Information Technology, Data Science, Engineering, Mathematics, or a related field, or equivalent work experienceMinimum 3-7 years of relevant work experienceExperience working within an Agile/SCRUM environmentExperience delivering and supporting machine learning and AI solutions in production environmentsExperience with the machine learning lifecycle, including development, deployment, monitoring, governance, and retirementExperience with Generative AI, Large Language Models (LLMs), and foundation modelsExperience building AI-powered applications and servicesExperience implementing Retrieval-Augmented Generation (RAG) solutionsExperience designing and implementing AI agents and agentic workflows with responsible AI and human oversight controlsExperience with machine learning libraries and frameworksExperience with AI orchestration frameworksExperience with vector databases, embeddings, and semantic search technologiesExperience with ML Ops, AI Ops, and model lifecycle management frameworksExperience working with databases, data lakes, and modern data platformsExperience building cloud-native applications, services, CI/CD pipelines, and Infrastructure as Code (IaC)Experience with cloud computing platforms, specifically Amazon Web Services technologies such as SageMaker, Bedrock, Docker, Lambda, and KubernetesExperience troubleshooting production applications, AI systems, and cloud infrastructureExperience integrating AI solutions with enterprise systems, APIs, and third-party platformsStrong Python programming experienceKnowledge & AbilitiesAn ability to adopt new ways of working and embrace new technologies and techniquesDynamic communication skills with a focus on building relationships by listening and asking questionsExtensive and effective presentation skillsStrong leadership and team building skillsAbility to establish and communicate priorities, constraints, deadlines, and goalsAbility to develop strong trust relationships with stakeholdersStrong organizational skills and experience with formal SDLCAbility to solve complex problems and think analyticallyHighly self-motivated and directedAbility to absorb and retain information quicklyAbility to present technical concepts in user-friendly languagePossess an agile mindset and openness to adaptation based on experience and feedbackStrong attention to detail and a focus on long-term strategic qualityPromote a culture of collaboration, innovation, and continuous improvementConsistently participate in diversity, equity and inclusion (DEI) events and use a DEI perspective to enhance the culture and positively impact our business, members, and communitiesStrong understanding of machine learning, deep learning, Generative AI, LLMs, embeddings, vector search, prompt engineering, and RAGUnderstanding of AI agent architecture, tool use, multi-agent patterns, and workflow orchestrationUnderstanding of Responsible AI principles, model governance, AI risk management, security, privacy, and regulatory considerationsAbility to design AI solutions that balance innovation and business value with security, compliance, human oversight, and operational reliabilityAbility to evaluate AI, ML, and agent performance using quantitative and qualitative methodsAbility to troubleshoot model drift, hallucinations, unintended agent behavior, performance degradation, and operational issuesLinux and Windows systems administration