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Vice President, Production Support Analyst\-AI Adoption\-Liquidity \& Margin Service Platform
At BNY, our culture allows us to run our company better and enables employees’ growth and success. As a leading global financial services company at the heart of the global financial system, we influence nearly 20% of the world’s investible assets. Every day, our teams harness cutting\-edge AI and breakthrough technologies to collaborate with clients, driving transformative solutions that redefine industries and uplift communities worldwide.
Recognized as a top destination for innovators, BNY is where bold ideas meet advanced technology and exceptional talent. Together, we power the future of finance – and this is what \#LifeAtBNY is all about. Join us and be part of something extraordinary.
We’re seeking a future team member for the role of Vice President, Production Support Analyst\-AI Adoption\-Liquidity \& Margin Service Platform to join our Liquidity and Margin Services team in the Markets space. This role is located in Pittsburgh
In this role, you’ll make an impact in the following ways:
- Provide production services support for critical Liquidity and Margin Services applications, ensuring platform stability, availability, and operational resilience
- Act as the first line of defense for supported applications, proactively monitoring systems, alerts, dashboards, and batch processes to identify and mitigate risks before business impact occurs
- Investigate and resolve production incidents, user issues, and service requests raised by Operations, Product, and Client Service teams
- Perform incident triage, impact assessment, root cause analysis, and escalation to appropriate development teams when required
- Serve as the bridge between business users and application development teams , ensuring clear communication, timely updates, and effective issue resolution
- Support production change activities, including validation, implementation readiness, post\-change checks, and adherence to change governance processes
- Maintain accurate operational records through ServiceNow incidents (INC) , change requests (RFC) , and related support documentation
- Support and maintain monitoring configurations, alerting thresholds, and operational runbooks to improve early detection and response capabilities
- Partner with development, infrastructure, middleware, database, and enterprise support teams in a platform operating model to deliver reliable service outcomes
- Contribute to continuous improvement efforts by identifying recurring issues, automation opportunities, and process optimization initiatives
- Provide timely status communications to internal stakeholders during incidents, outages, and priority production events
- Support applications across the Liquidity and Margin Services portfolio
- Leverage AI\-enabled tools and modern support practices to improve investigation efficiency, knowledge management, and operational productivity
- Create and maintain knowledge articles, support guides, FAQs, Confluence documentation, and troubleshooting notes for team\-wide use
To be successful in this role, we’re seeking the following:
- Bachelor’s degree in computer science, engineering, information systems, or a related discipline, or equivalent work experience required
- Significant experience in production support, application support, or production services within financial services or a comparable high\-availability environment
- Strong understanding of incident, problem, and change management processes
- Experience using service management and workflow tools such as ServiceNow for incidents, change requests, and operational tracking
- Hands\-on experience with application monitoring, alert management, and observability tooling
- Ability to investigate application failures, data issues, job failures, interface breakdowns, and environment\-related incidents across distributed systems
- Strong analytical and troubleshooting skills, with the ability to assess impact, identify probable causes, and coordinate resolution across multiple teams
- Experience supporting business\-critical applications in a fast\-paced, time\-sensitive production environment
- Strong communication and stakeholder management skills, with the ability to provide concise updates to technical and non\-technical audiences
- Ability to work closely with Operations, Product, Client Service, and development teams while maintaining strong ownership and accountability
- Knowledge of middleware, messaging, scheduling, or integration patterns such as MQ, Kafka, APIs, file transfers, or batch processing is preferred
- Familiarity with database querying and analysis using SQL is preferred
- Comfort working across Windows and Linux environments
- Experience with scripting or automation for support efficiency is a plus
- Familiarity with Confluence , knowledge repositories, operational playbooks, and support documentation practices
- Understanding of production controls, application health checks, alert tuning, and operational risk reduction
- Ability to remain calm under pressure, manage priorities effectively, and drive incidents through to resolution
- Experience using AI tools such as GitLab , Windsurf , Copilot , or similar productivity tools is a plus
At BNY, our culture speaks for itself, check out the latest BNY news at:
BNY Newsroom
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Here’s a few of our recent awards:
- America’s Most Innovative Companies, Fortune, 2025
- World’s Most Admired Companies, Fortune, 2025
- “Most Just Companies”, Just Capital and CNBC, 2025
Our Benefits and Rewards:
BNY offers highly competitive compensation, benefits, and wellbeing programs rooted in a strong culture of excellence and our pay\-for\-performance philosophy. We provide access to flexible global resources and tools for your life’s journey. Focus on your health, foster your personal resilience, and reach your financial goals as a valued member of our team, along with generous paid leaves, including paid volunteer time, that can support you and your family through moments that matter.
BNY is an Equal Employment Opportunity/Affirmative Action Employer \- Underrepresented racial and ethnic groups/Females/Individuals with Disabilities/Protected Veterans.
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
BNY AI Hiring
BNY has 4 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Boston, MA, US, Pittsburgh, PA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
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