AI Engineer

General Function

The Principal AI Engineer designs and builds artificial intelligence systems that address complex business challenges and deliver measurable valuewith a focus on agentic AI. This role functions as a forward-deployed engineer partnering with product owners, lines-of-business, engineering, security, risk, and compliance to identify high-impact opportunities, and deliver safe and effective agentic AI solutions.

This role focuses on AI for the Customer by using generative AI predominantly in AWS to deliver operational efficiency and superior customer experience. The Principal AI Engineer develops AI agents that leverage reasoning, planning, retrieval, and tools to complete complex tasks; establishes evaluation and documentation practices; monitors production performance; and produces evidence needed for governance.

This role communicates complex AI concepts, risks, opportunities, and recommendations clearly to technical teams, business stakeholders, and senior leaders while supporting the Bank’s business goals and remaining within risk appetite.

Essential Duties and Responsibilities

AI Development and Implementation

• Design and build generative AI and/or agentic systems that solve business problems and deliver measurable value

• Develop AI agents that use reasoning, planning, retrieval, and tool integration to complete complex tasks

• Evaluate AI agents before and after their deployment to drive continuous improvement and ensure effectiveness of AI systems

• Research, prototype, and apply emerging AI models, agent frameworks, orchestration patterns, and enterprise development practices 
   

Analytics and Insights

• Design and execute experiments to evaluate prompts, retrieval strategies, agent workflows, tool integrations, and user experience

• Conduct data analysis to compare prompts, workflows, and tools across runtime, accuracy, and output quality; report results for governance processes; and quantify return on investment for AI solutions

• Use supervised and unsupervised learning techniques to understand customer needs on the channels we support to inform decision making.

• Define success metrics for each AI use case, such as containment, time to resolution, customer satisfaction, or sentiment.

• Monitor agent performance in production, including latency, failure rates, tool-call success, escalation rates, retry behavior, and cost per interaction

• Produce evidence for AI governance reviews, including evaluation results, control effectiveness, model/tool performance, and risk mitigation outcomes
   

Collaboration and Communication

• Deliver complex AI projects and cross-team initiatives with limited supervision, ensuring solutions are integrated, scalable, and compliant

• Serve as a technical authority for colleagues by influencing architectural decisions, and shaping engineering standards across products and platforms

• Partner with product owners and line-of-business teams to understand day-to-day workflows, identify automation opportunities, and translate business needs into well-defined AI and agentic use cases with clear objectives, constraints, and success criteria

• Educate technical teams, business stakeholders, and senior leaders on AI concepts, solution designs, recommendations, opportunities, and emerging trends through clear, practical guidance

• Collaborate with software engineers, cloud engineers, data engineers, and data scientists to integrate AI solutions into existing systems and products

• Work with compliance, security, and risk teams to ensure AI systems meet governance, regulatory, and responsible AI requirements before and after deployment
   

Minimum Knowledge, Skills and Abilities Required

Education and Experience

• Bachelor's degree in Computer Science, Statistics, Data Science, Mathematics, or related technical field; advanced degree preferred but not required 

• 6+ years of experience developing software or AI solutions in production environments 
   

Technical Skills

• Strong programming skills in Python, Typescript, Java, Go, or similar languagesused for AI engineering and software development

• Expertise in generative AI techniques, e.g. context engineering, LLM fine-tuning, retrieval-augmented generation (RAG), evaluation frameworks, model context protocol (MCP), loop/graph engineering, skills/open knowledge format, and agentic system design

• Experience with cloud computing platforms, preferred (especially AWS)

• Familiarity with prompt optimization, retrieval tuning, caching, and model selection to improve generative AI performance, latency, cost, and reliability

• Knowledge of agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Strands, Google ADK,Anthropic sdk, or OpenAI sdk
   

Engineering Practices

• Proficiency with version control systems (Git/GitHub)

• Understanding of CI/CD pipelines and DevOps practices

• Experience with containerization and Infrastructure as Code (Docker, Terraform)

• Knowledge of data structures, algorithms, and software design principles

• Experience designing observability systems for AI applications preferred
   

Business and Soft Skills

• Demonstrates curiosity and adaptability by quickly learning new skills, techniques, tools, and frameworks as AI technologies and business needs evolve.

• Communicates clearly, builds consensus, and presents ideas effectively in written and verbal formats.

• Promotes knowledge sharing and supports strong cross-team collaboration.

• Uses sound analytical judgment to solve problems, prioritize competing demands, and work independently.

• Works well in Agile environments and with standard business productivity tools.

• Understands ethical considerations and responsible practices for AI deployment.