WhiteCrow Research
Website:
whitecrowresearch.com
Company:
https://www.linkedin.com/company/whitecrow-research-pvt--ltd
Seniority: Executive
Industries: Financial Services
Job details:
About WhiteCrow
We are global talent research, insight, and sourcing specialists with offices in the UK, USA, Singapore, Malaysia, Hong Kong, Dubai, and India. Our international reach has helped us to understand and penetrate specialist markets at a global level. In addition to this, our service is also extended to complement our client’s in-house talent acquisition teams.
About our client
Our client operates in the consumer financial services space, focusing on making everyday purchases and essential needs more accessible through flexible financing solutions. They support individuals across their financial journey—from obtaining their first line of credit to managing long-term financial flexibility—by enabling more informed and responsible credit decisions.
Our client has built a vast network that connects consumers with a wide range of small and mid-sized businesses, as well as providers in the health and wellness sector. Through this ecosystem, they play a meaningful role in supporting both customer financial well-being and the growth of businesses that form a critical part of the broader economy.
As a VP, Engineering Manager, you will be responsible for...
People, Talent and Cross-Functional Collaboration:
- Ensuring optimal staffing levels across front-end, back-end, full-stack, and platform specializations.
- Hiring, coaching, and developing top-tier engineering talent.
- Managing direct reports and technical leads, addressing performance issues promptly, and building strong relationships with Product, Design, Platform, Data, and agile train leadership to deliver cohesive, customer-focused solutions.
- 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.
- Fostering best practice adoption across the engineering community with AI tooling treated as a first-class part of the practice.
- Defining standards for responsible AI adoption — including when to use AI coding assistants, agents, skills, and MCP integrations versus traditional approaches.
- Establishing clear guidance on the distinction between AI Skills (deterministic capabilities) and Agents (autonomous, multi-step workflows).
- Evaluating, piloting, and rolling out AI tools (GitHub Copilot, Claude Code, Cursor, Windsurf, Continue.dev, Aider, Cline, Ollama).
- Defining governance for AI use in regulated financial services contexts including data handling, PII protection, IP compliance, and code provenance.
- Tracking and measuring the combined impact of CoP practices and AI adoption on cycle time, PR throughput, defect rates, and developer satisfaction.
- System Architecture and Technical Direction: Owning a modern view of system architecture spanning client, server, data, integration, and AI layers.
- Championing patterns such as micro-frontends, BFF, event-driven, API-first, serverless, and composable architectures.
- Partnering with Enterprise Architects on platform alignment.
- Guiding trade-off decisions (monolith vs. microservices, sync vs. async, build vs. buy). Using AI-powered architecture tools to accelerate design exploration and generate ADRs.
- Ensuring teams document decisions so both humans and AI agents can reason about the system.
- Understanding how AI tools interact with system architecture — including RAG, vector databases, MCP integrations, and agentic workflows.
Strategic Planning, Delivery and Operational Excellence:
- Ensuring plans align with CoP goals, reviewing quarterly commitments to flag risks early, and step in to resolve conflicts between product, design, and engineering.
- Driving CI/CD best practices, ensuring high-quality scalable solutions across the stack, and provide on-call support during escalations.
- Supporting tech leads hands-on when needed, regardless of which layer of the stack the problem lives in.
What you already have...
- 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.
- Experience with AWS cloud services, CI/CD pipelines, and modern DevOps practices.
- Proven ability to lead engineers working across the full stack.
- 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).
- Experience making and documenting architecture decisions (ADRs), with understanding of how AI-native architecture (RAG pipelines, vector databases, agent orchestration) differs from traditional architecture.
- 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.
- Awareness of the open-source AI tooling ecosystem (Continue.dev, Aider, Cline, Ollama).
- 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.
Click on Apply to know more.
