AI/ML Engineer

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 Bank’s 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