AVP, Principal Software Engineer (Front-End)

VP, Engineering Manager

Responsibilities: <ul><li>Lead development teams through all phases of the Agile development life cycle.</li><li>Coach and mentor junior talent, including pair-programming and reviewing AIgenerated code with engineering judgment.</li><li>Influence and collaborate with software developers, UX designers, product managers, application architects, and customer application experts to create</li><li>amazing apps.</li><li>Champion responsible adoption of AI coding assistants (GitHub Copilot, Claude Code, Cursor) on the team—setting prompt patterns, distinguishing AI Skills</li><li>(deterministic capabilities) from Agents (autonomous, multi-step workflows), and following company guidance for data handling, PII protection, IP compliance, and code provenance in regulated financial services contexts.</li><li>Ensure compliance with architecture, security, and technology standards set by company.</li><li>Participate in addressing root cause analyses, incident reports, and provide direction in problem resolution.</li><li>Ability to analyze production defects, troubleshoot systems, identify root cause, and implement fixes.</li><li>Provide 24x7 on-call support periodically throughout the year.</li><li>Identify ways to increase efficiency and improve system performance, including by piloting AI tools and agentic workflows for repetitive engineering tasks.</li><li>Work with third party vendors to develop software and/or integrate their software into company products.</li></ul> Requirements: <ul><li>Bachelor’s degree and a minimum of 5 years in full stack development with a strong focus on JavaScript (React JS in particular) OR, in lieu of degree, with 7 years of experience in the same.</li><li>Hands-on experience using at least one AI coding assistant (GitHub Copilot, Claude Code, Cursor, or equivalent) as part of regular development workflow.</li><li>Proficiency in TypeScript, JavaScript, React, Redux, Styled-Components, and RTL or Cypress.</li><li>Familiarity with AI-native concepts—RAG patterns, vector databases, MCP (Model Context Protocol), and agent orchestration—and how they differ from</li><li>traditional architecture.</li><li>Awareness of the open-source AI tooling ecosystem (Continue.dev, Aider, Cline, Ollama). Exposure to building or customizing AI agents/skills using LLM APIs (OpenAI, Anthropic Claude, Azure OpenAI) for engineering automation such as code review, documentation, or migration assistants.</li><li>Live and breathe testing.</li></ul>

Responsibilities: People, Talent and Cross-Functional Collaboration:&nbsp; <ul><li>Ensure optimal staffing levels across front-end, back-end, full-stack, and platform specializations.&nbsp;</li><li>Hire, coach, and develop top-tier engineering talent.</li><li>Manage direct reports and technical leads, address performance issues promptly, and build strong relationships with Product, Design, Platform, Data, and agile train leadership to deliver cohesive, customer-focused solutions.</li><li>Community of Practice, Developer Experience and AI-Driven Engineering: As AI tooling becomes inseparable from modern engineering practice, own the convergence of Community of Practice standards and AI adoption as a single discipline.&nbsp;</li><li>Foster best practice adoption across the engineering community with AI tooling treated as a first-class part of the practice.</li><li>Define standards for responsible AI adoption — including when to use AI coding assistants, agents, skills, and MCP integrations versus traditional approaches.</li><li>Establish clear guidance on the distinction between AI Skills (deterministic capabilities) and Agents (autonomous, multi-step workflows). </li><li>Evaluate, pilot, and roll out AI tools (GitHub Copilot, Claude Code, Cursor, Windsurf, Continue.dev, Aider, Cline, Ollama). </li><li>Define governance for AI use in regulated financial services contexts including data handling, PII protection, IP compliance, and code provenance. </li><li>Track and measure the combined impact of CoP practices and AI adoption on cycle time, PR throughput, defect rates, and developer satisfaction.</li><li>System Architecture and Technical Direction: Own a modern view of system architecture spanning client, server, data, integration, and AI layers.&nbsp;</li><li>Champion patterns such as micro-frontends, BFF, event-driven, API-first, serverless, and composable architectures.&nbsp;</li><li>Partner with Enterprise Architects on platform alignment.&nbsp;</li><li>Guide trade-off decisions (monolith vs. microservices, sync vs. async, build vs. buy). Use AI-powered architecture tools to accelerate design exploration and generate ADRs.</li><li>Ensure teams document decisions so both humans and AI agents can reason about the system. </li><li>Understand how AI tools interact with system architecture — including RAG, vector databases, MCP integrations, and agentic workflows.</li></ul> Strategic Planning, Delivery and Operational Excellence:&nbsp; <ul><li>Ensure plans align with CoP goals, review quarterly commitments to flag risks early, and step in to resolve conflicts between product, design, and engineering.&nbsp;</li><li>Drive CI/CD best practices, ensure high-quality scalable solutions across the stack, and provide on-call support during escalations.&nbsp;</li><li>Support tech leads hands-on when needed, regardless of which layer of the stack the problem lives in.</li></ul> Requirements: <ul><li>Strong hands-on background in modern front-end technologies (ReactJS, TypeScript, NextJS, Cypress) as an anchor foundation, with working knowledge of back-end technologies, APIs, microservices, and data layer concepts.&nbsp;</li><li>Experience with AWS cloud services, CI/CD pipelines, and modern DevOps practices.&nbsp;</li><li>Proven ability to lead engineers working across the full stack.</li><li>Solid grounding in system architecture fundamentals — distributed systems, scalability, CAP trade-offs, caching, messaging, API design — and familiarity with modern patterns (micro-frontends, BFF, event-driven, CQRS, serverless, composable architectures).&nbsp;</li><li>Experience making and documenting architecture decisions (ADRs), with understanding of how AI-native architecture (RAG pipelines, vector databases, agent orchestration) differs from traditional architecture.</li><li>Hands-on experience with AI coding assistants (GitHub Copilot, Claude Code, Cursor, or equivalent) with clear understanding of the difference between AI Skills and Agents, familiarity with MCP (Model Context Protocol), RAG patterns, and experience defining AI usage guidelines and governance frameworks for regulated environments.&nbsp;</li><li>Awareness of the open-source AI tooling ecosystem (Continue.dev, Aider, Cline, Ollama).</li><li>Proven experience leading and growing high-performing engineering teams, running or contributing to a Community of Practice, and strong communication skills with the ability to influence across organizational levels.</li></ul>

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AVP, Principal Software Engineer (Front-End)

VP, Engineering Manager

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VP, Engineering Manager

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