AI/ML Engineer

Job Description

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 & Experience

Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, Mathematics, or a related field, or equivalent work experience

Minimum 3-7 years of relevant work experience

Experience working within an Agile/SCRUM environment

Experience delivering and supporting machine learning and AI solutions in production environments

Experience with the machine learning lifecycle, including development, deployment, monitoring, governance, and retirement

Experience with Generative AI, Large Language Models (LLMs), and foundation models

Experience building AI-powered applications and services

Experience implementing Retrieval-Augmented Generation (RAG) solutions

Experience designing and implementing AI agents and agentic workflows with responsible AI and human oversight controls

Experience with machine learning libraries and frameworks

Experience with AI orchestration frameworks

Experience with vector databases, embeddings, and semantic search technologies

Experience with ML Ops, AI Ops, and model lifecycle management frameworks

Experience working with databases, data lakes, and modern data platforms

Experience 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 Kubernetes

Experience troubleshooting production applications, AI systems, and cloud infrastructure

Experience integrating AI solutions with enterprise systems, APIs, and third-party platforms

Strong Python programming experience

Knowledge & Abilities

An ability to adopt new ways of working and embrace new technologies and techniques

Dynamic communication skills with a focus on building relationships by listening and asking questions

Extensive and effective presentation skills

Strong leadership and team building skills

Ability to establish and communicate priorities, constraints, deadlines, and goals

Ability to develop strong trust relationships with stakeholders

Strong organizational skills and experience with formal SDLC

Ability to solve complex problems and think analytically

Highly self-motivated and directed

Ability to absorb and retain information quickly

Ability to present technical concepts in user-friendly language

Possess an agile mindset and openness to adaptation based on experience and feedback

Strong attention to detail and a focus on long-term strategic quality

Promote a culture of collaboration, innovation, and continuous improvement

Consistently participate in diversity, equity and inclusion (DEI) events and use a DEI perspective to enhance the culture and positively impact our business, members, and communities

Strong understanding of machine learning, deep learning, Generative AI, LLMs, embeddings, vector search, prompt engineering, and RAG

Understanding of AI agent architecture, tool use, multi-agent patterns, and workflow orchestration

Understanding of Responsible AI principles, model governance, AI risk management, security, privacy, and regulatory considerations

Ability to design AI solutions that balance innovation and business value with security, compliance, human oversight, and operational reliability

Ability to evaluate AI, ML, and agent performance using quantitative and qualitative methods

Ability to troubleshoot model drift, hallucinations, unintended agent behavior, performance degradation, and operational issues

Linux and Windows systems administration