AI Engineer – Generative AI & Custom Agent Development - Tier 1 Banking Client

AI Engineer – Generative AI & Custom Agent Development - Tier 1 Banking Client

Duration: 8 Months

Location: Remote (1x/Month in Toronto office)

Overview:

Our retail banking client's technology group is building a new delivery team made up of full-stack developers and AI engineers. The AI Engineer will design and build custom agents and AI-enabled applications that help the team automate engineering, testing, and business workflows while delivering digital solutions for financial products, including mortgage-pricing capabilities.

This is a capability-first search. Years of experience will not outweigh hands-on evidence. A candidate can be highly competitive if they can show that they have built an AI-enabled solution from scratch, integrated it into a usable application, and taken it through productionization or equivalent operational readiness.

Responsibilities:

• Design, build, and productionize custom AI agents that can reason over instructions, use tools or APIs, and automate meaningful workflows.
• Develop AI-enabled applications and services from initial proof of concept through integration, testing, deployment readiness, and ongoing improvement.
• Apply GitHub Copilot or comparable generative-AI technologies to accelerate development while maintaining engineering quality and appropriate human review.
• Create agents that generate test cases or test scripts and support automated testing activities across the delivery lifecycle.
• Integrate large language models with application services, enterprise APIs, approved data sources, and user-facing web experiences.
• Define prompts, tools, workflows, evaluation criteria, error handling, and fallback behavior for reliable agent execution.
• Assess model and agent performance, identify failure modes, and improve accuracy, safety, latency, cost, and maintainability.
• Collaborate with Java, Angular, and AWS engineers to embed AI capabilities into broader full-stack solutions.
• Identify high-value automation opportunities and recommend practical ways to simplify work for engineering and business leadership.
• Contribute to Agile planning, technical documentation, demos, code reviews, and team knowledge sharing.
• Take ownership, work effectively through ambiguity, and continue learning as the team's AI platform and use cases evolve.

Must Haves:

• Demonstrated hands-on experience building generative-AI applications or agentic solutions.
• Direct experience creating custom agents or agent workflows, beyond basic use of a chatbot or code-completion tool.
• Strong programming ability in Python, Java, or TypeScript, with the ability to build clean services, integrations, and application logic; Python is preferred for AI-focused development.
• Practical experience integrating large language models through APIs and applying prompt engineering, tool or function calling, workflow orchestration, and structured outputs.
• Experience taking an AI solution beyond a demonstration into a usable, supportable, or production-ready state.
• Understanding of AI quality and operational concerns such as evaluation, hallucination or failure handling, guardrails, observability, privacy, security, latency, and cost.
• Experience integrating AI solutions with APIs, enterprise data sources, web applications, or automated workflows.
• Ability to explain personal technical contributions clearly, demonstrate working solutions, and make sound engineering recommendations.
• Strong initiative, collaboration, communication, curiosity, and willingness to learn in a newly forming team.

Nice to have:

• Experience with agent frameworks or orchestration libraries such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, or comparable technologies.
• Experience with retrieval-augmented generation, embeddings, vector search, grounding, or enterprise knowledge integrations.
• Experience building AI-enabled web applications or collaborating within an Angular and Java full-stack environment.
• AWS experience, including serverless integrations using AWS Lambda.
• Experience building testing agents, synthetic test generation, or AI-assisted quality-engineering workflows.
• Financial-services experience, particularly within personal banking, lending, mortgages, or pricing; this is helpful but not mandatory.