Interested in this AI/ML Engineer role at BNY?
Apply Now →About This Role
As an AI Program Lead at BNY, you will lead a cross-functional team focused on identifying, developing, deploying, and scaling AI-driven solutions that address business needs and improve operational efficiency. You will act as the primary point of leadership for the POD, guiding strategy, execution, and delivery while ensuring alignment with BNY’s enterprise AI priorities, governance standards, and risk framework.
In this role, you will bridge business needs with artificial intelligence, analytics, and operational transformation capabilities. You will partner closely with AI Hub, data scientists, engineers, Ops Hub, platform teams, and business stakeholders to shape AI opportunities, prioritize use cases, and deliver practical, responsible AI applications. You will also provide day-to-day leadership to POD team members, fostering collaboration, innovation, and continuous improvement.
Key Responsibilities
AI Strategy, Delivery & Execution
- Lead the identification and prioritization of business processes where AI/ML, analytics, and automation can deliver measurable value.
- Oversee the end-to-end development, validation, deployment, and monitoring of AI and machine learning solutions.
- Guide the team in data preparation, feature engineering, exploratory analysis, and solution design to support effective modeling efforts.
- Ensure AI solutions are scalable, practical, and aligned to business objectives, operational needs, and enterprise architecture standards.
- Monitor solution performance post-deployment and drive continuous improvement opportunities.
POD Leadership & Team Management
- Lead, coach, and develop POD team members, creating a high-performing team environment focused on delivery, innovation, and accountability.
- Set clear priorities, assign work across the POD, and ensure effective execution against timelines and business commitments.
- Serve as the primary escalation point for delivery, resource, and execution issues within the POD.
- Foster collaboration across business, operations, and technology stakeholders to remove blockers and maintain momentum.
- Provide mentorship to team members on AI/ML practices, business analysis, and stakeholder engagement.
Cross-Functional Collaboration
- Partner with product owners, AI Hub, Ops Hub, platform teams, and business units to define requirements and ensure AI solutions achieve intended business outcomes.
- Translate complex technical concepts into clear, actionable insights for non-technical stakeholders and senior leadership.
- Work closely with data engineering and technology teams to integrate AI solutions into scalable production workflows.
- Facilitate alignment between stakeholders on objectives, scope, risks, dependencies, and success measures for AI initiatives.
Governance & Responsible AI
- Ensure POD activities adhere to responsible and ethical AI practices in line with enterprise governance frameworks.
- Oversee testing, validation, and documentation to support model accuracy, fairness, transparency, and compliance.
- Partner with control, risk, and compliance stakeholders to ensure AI solutions meet applicable standards and review requirements.
- Promote disciplined delivery practices that balance innovation with strong risk management and control execution.
Innovation, Change & Thought Leadership
- Stay current with emerging AI trends, tools, and techniques, and assess their relevance to business opportunities within the POD.
- Drive research into new methodologies and present recommendations to enhance internal AI capabilities.
- Champion adoption of AI solutions by helping stakeholders understand business value, change impacts, and implementation considerations.
- Contribute to the broader AI community within BNY by sharing insights, best practices, and lessons learned across teams.
Qualifications
Education & Experience
- Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
- Prior experience in AI/ML, analytics, data science, or a related technical discipline.
- Demonstrated experience leading projects, workstreams, or teams in a cross-functional environment.
- Experience working with business stakeholders to translate strategic priorities into practical technology or analytics solutions.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At BNY, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
BNY AI Hiring
BNY has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Engineer, Data Scientist. Positions span Pittsburgh, PA, US, Jersey City, NJ, US, New York, NY. Compensation range: $240K - $240K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.