Vice President, AI / Machine Learning Data Engineering
At a glance
VP-level Machine Learning Engineering role at BNY External Career Site. New York · full-time · no salary stated · we estimate $213,075–$286,747 across the United States.
BNY External Career Site has not published a salary for this role. The figure below is our estimate, not theirs.
aimlroles summary, based on the employer's posting.
What you'll do
- Design and build applied AI products, while providing hands-on technical direction for machine learning work
- Research and test model and tooling options, then turn the results into recommendations and delivery plans
- Work with engineering and business partners to identify priorities and define measures of developer and business impact
- Apply AI to software development workflows, delivering secure, scalable tools for builders, designers and developers
- Mentor engineers and strengthen architecture, reliability and responsible AI practices across delivery
What you bring
- A bachelor's in computer science, data engineering or a related area, or equivalent education and experience
- Five to nine years in software development, plus advanced applied AI/ML and software engineering experience
- Experience developing models and putting AI solutions into production
- Strong grasp of enterprise engineering, MLOps or LLMOps, model evaluation and secure, scalable AI design
Who this fits
This is a hands-on VP-level technical leadership role for someone who can guide AI engineering decisions while building production solutions. You’ll work across engineering, product and business audiences, and mentor engineers. The position is based in New York, NY.
From the employer
We’re seeking a future team member for the role of Vice President, AI / Machine Learning Engineer to join our AI Hub team. This role is located in New York, NY.
This role is located in our AI Hub and is focused on building AI-powered tools and platforms that improve the way engineers design, build, test, and operate software. This is a strong opportunity for a hands-on engineer who can apply deep AI and software engineering expertise to deliver practical solutions, guide technical direction, and help scale high-value capabilities in an enterprise environment.
In this role, you’ll make an impact in the following ways:
- Lead the design and development of applied AI solutions, providing hands-on technical leadership for machine learning and artificial intelligence products.
- Bring strong knowledge of Applied AI, LLMs, fine tuning, model evaluation, and approaches to building fit-for-purpose models, copilots, and agents for engineering use cases.
- Conduct technical research, prototyping, and experimentation to evaluate model and tooling approaches, and translate findings into actionable recommendations and implementation plans.
- Partner with engineering and business stakeholders to understand priorities, workflows, and opportunities, and help define success measures that demonstrate developer and business impact.
- Bring expertise in applying AI to improve the software development lifecycle and support builders, designers, and developers with secure, scalable, and production-ready capabilities.
- Contribute to the achievement of Application Development objectives by mentoring engineers, promoting strong engineering practices, and supporting high-quality delivery across architecture, reliability, and responsible AI adoption.
To be successful in this role, we’re seeking the following:
- Bachelor's degree in Computer Science, Data Engineering, or a related field or the equivalent combination of education and experience required.
- 5-9 years of experience in software development required; experience in the securities or financial services industry is a plus.
- Advanced experience, preferably 8+ years, in applied AI/ML, model development, and software engineering, including experience building and deploying production solutions.
- Ability to operate as a hands-on technical lead, collaborating with engineers and partners while helping drive design, implementation, and delivery decisions.
- Strong understanding of enterprise software practices, MLOps / LLMOps, evaluation approaches, and how to create secure, scalable, and sustainable AI solutions.
- Ability to communicate technical ideas effectively and work across a range of audiences, including engineers, product partners, and business stakeholders.