Manager, Software Engineer (AI)

Vestwell•Austin, NY

5d•Hybrid

About The Position

Vestwell is the financial technology company powering the new savings economy. Our platform redefines how people save for the critical aspects of life across retirement, education, and healthcare savings needs. Today, Vestwell enables over 350,000 businesses and over 2M active savers, with over $50B in assets saved across all 50 states. Vestwell's platform serves a diverse clientele, including financial advisers, employers, third-party administrators, financial institutions, payroll providers, government agencies, and individual savers. To learn more, visit vestwell.com. The Engineering team is seeking an experienced Engineering Manager to lead our AI Engineering organization. This leader will be responsible for building and scaling the engineering team that delivers AI-powered products and enterprise capabilities. You will combine strong technical leadership with exceptional people management, partnering closely with Product, Design, Data, and business leaders to define strategy, execute against ambitious roadmaps, and deliver reliable, production-grade AI solutions. The ideal candidate has a proven track record of leading high-performing engineering teams while remaining technically credible in modern AI and software engineering. You know how to balance execution with long-term architecture, mentor engineers, raise the engineering bar, and build systems that scale. You’re a great fit if you enjoy developing people as much as technology, can navigate ambiguity, and are passionate about delivering AI solutions that create measurable business value.

Requirements

Bachelor’s degree in Computer Science, Engineering, Physics, Mathematics, or a related technical field.
8+ years of professional software engineering experience, including at least 3+ years leading engineering teams.
Proven experience building, delivering, and operating production software systems at scale.
Experience leading engineering teams responsible for AI/ML-enabled products or platforms.
Strong hands-on experience developing production applications in Python.
Deep understanding of modern AI and machine learning concepts, including large language models (LLMs), retrieval-augmented generation (RAG), AI agents, and model orchestration frameworks.
Experience integrating AI services using leading LLM providers and modern AI frameworks such as LangChain, LangGraph, or similar technologies.
Experience designing cloud-native applications using AWS, Docker, and modern deployment and CI/CD practices.
Experience with relational databases (PostgreSQL preferred) and distributed application architectures.
Strong understanding of software engineering best practices, including testing, observability, security, code review, and operational excellence.
Demonstrated ability to recruit, mentor, and develop high-performing engineering teams.
Excellent communication and collaboration skills with the ability to influence technical and non-technical stakeholders.
Experience working in Agile development environments using tools such as GitHub, Jira, and Jenkins (or equivalent).

Nice To Haves

Experience in financial technology or regulated industries.
Startup or high-growth company experience.
Experience building and scaling AI platforms used across multiple products or business functions.
Experience leading engineering organizations through rapid growth or organizational change.

Responsibilities

Lead, mentor, and grow a team of AI software engineers, fostering a culture of technical excellence, ownership, and continuous learning.
Drive engineering execution by establishing priorities, managing delivery, and ensuring predictable, high-quality outcomes.
Define the technical architecture and engineering direction for AI platforms, services, and enterprise applications.
Partner with Product, Design, Data Science, Security, and business stakeholders to translate business objectives into scalable technical solutions.
Architect, build, and oversee the delivery of production-grade AI services with a focus on reliability, scalability, observability, and operational excellence.
Guide the implementation of modern AI capabilities, including retrieval-augmented generation (RAG), AI agents, orchestration frameworks, and integrations with leading LLM providers.
Establish engineering best practices for software development, testing, deployment, code quality, and operational support.
Remain technically engaged by reviewing architecture, mentoring engineers, and contributing to key technical decisions and complex implementations when appropriate.
Recruit, coach, and develop engineering talent while helping shape the long-term growth of the AI Engineering organization.