VP, Engineering Manager

Senior Software Engineer AI

Software Engineer AI

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>

Responsibilities: Design &amp; Build/Development &amp; Support <ul><li>Develop multi-agent workflow automation patterns using Agentic AI</li><li>Process redesign and mapping to agentic workflow patterns</li><li>Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python).</li><li>Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents</li><li>Profile inference latency, GPU/CPU utilization, and memory</li><li>Writes and maintains highly complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software/AI solutions</li><li>Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents</li><li>Leads the establishment and maintenance of comprehensive documentation for agents, applications, deployment processes and system configurations.</li></ul> Operational Excellence&nbsp;<ul><li>Own on-call runbooks, SLOs, and incident reviews; embed observability</li><li>Leads and mentors technical support and troubleshooting for highly complex issues with deployed applications to ensure minimal downtime and fast resolution.&nbsp;</li></ul> Collaboration &amp; Leadership <ul><li>Leads cross functional team of engineers to gather requirements and deliver solutions that meet business needs.</li><li>Experience working within product and agile delivery frameworks including backlog management and cross-functionality delivery and full E2E engineering.&nbsp;</li><li>Experience with enterprise architecture, data governance, or security standards in the context of digital solution delivery as part of E2E engineering and solution architecture.&nbsp;</li></ul> Capability &amp; Technology Stack Exposure <ul><li>Experience with digital technologies in the commercial &amp; sales management capability (e.g. Salesforce, Service Cloud, CPQ, Salesloft, ChatGPT or similar platforms)&nbsp;</li></ul> Requirements: <ul><li>Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.&nbsp;</li><li>Typically reflects 4 years or more with 3+ years of cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software development.&nbsp;</li></ul>

Responsibilities: Design &amp; Build/Development &amp; Support <ul><li>Develop multi-agent workflow automation patterns using Agentic AI</li><li>Process redesign and mapping to agentic workflow patterns</li><li>Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python).</li><li>Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents</li><li>Profile inference latency, GPU/CPU utilization, and memory</li><li>Writes and maintains highly complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software/AI solutions</li><li>Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents</li><li>Leads the establishment and maintenance of comprehensive documentation for agents, applications, deployment processes and system configurations.</li></ul> Operational Excellence&nbsp;<ul><li>Own on-call runbooks, SLOs, and incident reviews; embed observability</li><li>Leads and mentors technical support and troubleshooting for highly complex issues with deployed applications to ensure minimal downtime and fast resolution.&nbsp;</li></ul> Collaboration &amp; Leadership <ul><li>Leads cross functional team of engineers to gather requirements and deliver solutions that meet business needs.</li><li>Experience working within product and agile delivery frameworks including backlog management and cross-functionality delivery and full E2E engineering.&nbsp;</li><li>Experience with enterprise architecture, data governance, or security standards in the context of digital solution delivery as part of E2E engineering and solution architecture.&nbsp;</li></ul> Capability &amp; Technology Stack Exposure <ul><li>Experience with digital technologies in the commercial &amp; sales management capability (e.g. Salesforce, Service Cloud, CPQ, Salesloft, ChatGPT or similar platforms)&nbsp;</li></ul> Requirements: <ul><li>Minimum requirement of 2 years of building production software or data platforms.&nbsp;</li><li>Typically reflects 3 years or more with 2+ years of cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software development.&nbsp;</li></ul>

Skills

Industry

Location

Jobtitle

3 caching Jobs found

VP, Engineering Manager

Senior Software Engineer AI

Software Engineer AI

WhiteCrow
WhiteCrow is on a mission to connect companies with the best talent across the globe.

EMPLOYER

TALENT

COMPANY

Contact Us

JOBS BY SKILLS

JOBS BY FUNCTION

JOBS BY INDUSTRY

JOBS BY LOCATIONS

MODEL JOBS

JOBS BY CITY

Skills

Industry

Location

Jobtitle

3 caching Jobs found

VP, Engineering Manager

Senior Software Engineer AI

Software Engineer AI

WhiteCrow WhiteCrow is on a mission to connect companies with the best talent across the globe.

EMPLOYER

TALENT

COMPANY

Contact Us

JOBS BY SKILLS

JOBS BY FUNCTION

JOBS BY INDUSTRY

JOBS BY LOCATIONS

MODEL JOBS

JOBS BY CITY

WhiteCrow
WhiteCrow is on a mission to connect companies with the best talent across the globe.