VP Senior Applied AI Full-Stack Engineer

Citi•Mississauga, ON

10d•CA$120,800 - CA$170,800

About The Position

We are seeking a senior technical leader specializing in building high-performance, interactive data visualization platforms and enterprise reporting engines. This is an expert-level role focused on creating advanced, web-based analytical applications that combine rich, interactive frontends with highly optimized, data-intensive backends and distributed query engines. A mandatory and critical secondary focus of this role is the design and creation of custom Model Context Protocol (MCP) tools. You will build the bridge between LLMs/AI agents and our enterprise data layers, including Starburst, enabling AI to autonomously and securely query, analyze, and report on massive datasets. You will own the end-to-end design and delivery of sophisticated FinTech tools used directly by investment bankers, front-office traders, and financial analysts. This requires translating complex financial workflows into intuitive, high-fidelity web interfaces. Your primary focus will be on architecting and building highly interactive, web-based analytical dashboards using Angular and React, allowing users to slice, dice, and explore massive datasets in real-time. A key function is leveraging Starburst (Trino) to query massive, distributed datasets with sub-second latency. You will design and implement robust backend microservices (Java/Spring Boot) optimized for low-latency data retrieval, heavy aggregation, and streaming large datasets to the frontend. Furthermore, you will be responsible for designing and optimizing scalable ETL processes to manage huge volumes of financial data. A mandatory responsibility is to design, build, and maintain custom Model Context Protocol (MCP) servers and tools. This enables AI agents and LLMs to securely interface with internal databases, APIs, and reporting engines. You will also architect enterprise-grade document generation engines using the Aspose suite to programmatically create high-fidelity financial reports from complex templates. As a hands-on player/coach, you will deliver complex data visualizations and reporting engines while mentoring junior engineers. This includes critically reviewing and taking ownership of all AI-generated code (Java, Angular, React, SQL) to ensure structural integrity, security compliance, and production readiness, while applying a deep functional understanding of Corporate and Investment Banking (CIB) to all projects. This is not a conventional engineering role. You are expected to operate as a force multiplier — directing AI agents across multiple workstreams, validating output for production safety, and coaching others to do the same. The measure of success is not lines of code written, but outcomes delivered, quality maintained, and the team's collective capability raised.

Requirements

Strong foundation in systems analysis, design patterns, data analysis, and the full software development lifecycle (SDLC), including unit testing and CI/CD practices.
Extensive hands-on experience with Java and Spring Boot for building scalable, low-latency microservices and reactive data streaming APIs.
Strong, hands-on experience with Angular 19+, React, TypeScript, HTML5, and Web Sockets for building interactive, data-intensive dashboards.
Hands-on experience with Starburst (Trino) for querying massive, distributed datasets across federated enterprise data sources.
Deep experience with relational databases (Oracle, SQL Server) and NoSQL (MongoDB), including physical/logical database design, data modeling, and performance tuning.
Proven experience designing high-throughput ETL pipelines and optimizing data flows for real-time reporting.
Experience building document generation engines using the Aspose suite (Aspose.Words, Aspose.Cells, Aspose.PDF).
Demonstrated ability to design and build custom Model Context Protocol (MCP) servers to securely connect LLMs and AI agents to enterprise data.
Hands-on proficiency with AI coding tools (e.g., Claude Code, Cursor, GitHub Copilot) and the ability to write structured prompts and engineer high-quality context (schemas, API specs) for AI agents.
Strong experience with GitHub for source control and Jira for project management.
Functional understanding of Banking — able to articulate why their work matters, not just how it is built.

Nice To Haves

8+ years of relevant experience
Experience designing and implementing agentic AI workflows and integrating AI agents directly into CI/CD pipelines.
A strong understanding of event-driven systems and real-time message streaming platforms like Apache Kafka or RabbitMQ.

Responsibilities

Own the end-to-end design and delivery of sophisticated FinTech tools used directly by investment bankers, front-office traders, and financial analysts.
Translate complex financial workflows into intuitive, high-fidelity web interfaces.
Architect and build highly interactive, web-based analytical dashboards using Angular and React, allowing users to slice, dice, and explore massive datasets in real-time.
Leverage Starburst (Trino) to query massive, distributed datasets with sub-second latency.
Design and implement robust backend microservices (Java/Spring Boot) optimized for low-latency data retrieval, heavy aggregation, and streaming large datasets to the frontend.
Design and optimize scalable ETL processes to manage huge volumes of financial data.
Design, build, and maintain custom Model Context Protocol (MCP) servers and tools to enable AI agents and LLMs to securely interface with internal databases, APIs, and reporting engines.
Architect enterprise-grade document generation engines using the Aspose suite to programmatically create high-fidelity financial reports from complex templates.
Deliver complex data visualizations and reporting engines while mentoring junior engineers.
Critically review and take ownership of all AI-generated code (Java, Angular, React, SQL) to ensure structural integrity, security compliance, and production readiness.
Apply a deep functional understanding of Corporate and Investment Banking (CIB) to all projects.
Operate as a force multiplier — directing AI agents across multiple workstreams, validating output for production safety, and coaching others to do the same.