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The AI Garage for Investments & Wealth is seeking a Director, AI / Machine Learning Software Engineer to provide senior technical leadership in establishing and operationalizing enterprise-grade AI Operations (AI Ops) capabilities. This role is critical to ensuring that AI applications, agentic solutions, models, data pipelines, and supporting platforms can be deployed, governed, monitored, and sustained in production with the reliability, security, and operational rigor required in a highly regulated financial services environment.
This leader will serve as the bridge between Engineering and Production, providing oversight across the full AI lifecycle. The role is responsible for defining AI Ops standards, implementing automation and observability frameworks, establishing operational controls, and driving best practices for production support, resiliency, governance, and continuous improvement.
Given the breadth of expertise required across cloud platforms, automation, observability, AI/ML operations, and production reliability, this position requires a senior technical leader with the experience and authority to define strategy, influence architecture decisions, and drive operational excellence across the AI Garage and the broader Wealth & Investment Management Technology organization.
Key Responsibilities
- Define and lead the AI Ops strategy for AI Garage for Investments & Wealth.
- Establish standards, frameworks, and best practices for deploying, monitoring, and supporting AI/ML solutions in production.
- Provide technical leadership across the full AI lifecycle, from development through deployment, governance, and ongoing operations.
- Drive automation of AI/ML pipelines, model deployment, monitoring, alerting, and incident response processes.
- Build and enhance observability frameworks to ensure visibility into model performance, data health, system reliability, and operational risks.
- Partner with engineering, platform, infrastructure, data, risk, and production support teams to ensure AI solutions meet enterprise standards for resiliency, security, and compliance.
- Establish operational controls and governance processes for AI applications, models, and data pipelines.
- Lead efforts to improve production reliability, scalability, and efficiency for AI-powered applications and platforms.
- Influence architecture decisions to ensure AI solutions are designed for operational excellence in a regulated environment.
- Drive continuous improvement initiatives, including incident reviews, root cause analysis, and the adoption of lessons learned into engineering and operational practices.
Mentor and guide senior engineers and technical teams in AI Ops, SRE, and production support best practices. *
Required Qualifications
- Bachelor’s degree in Computer Science, Engineering, or a related technical field; advanced degree preferred.
- Significant experience in software engineering, machine learning engineering, AI/ML operations, or site reliability engineering.
- Proven experience leading enterprise-scale AI/ML production operations in a highly regulated environment.
- Strong expertise in cloud platforms, distributed systems, automation, CI/CD, and infrastructure as code.
- Experience with observability, monitoring, logging, and alerting frameworks for complex production environments.
- Deep understanding of AI/ML lifecycle management, including model deployment, monitoring, governance, and retraining processes.
- Strong knowledge of production support, incident management, resiliency engineering, and operational controls.
- Demonstrated ability to influence senior stakeholders, architecture decisions, and cross-functional technical strategy.
- Excellent communication and leadership skills, with the ability to bridge engineering and production teams effectively.
Preferred Qualifications
- Experience within financial services or another highly regulated industry.
- Familiarity with governance, risk, and compliance considerations for AI/ML applications.
- Experience supporting agentic AI solutions, modern ML platforms, and large-scale data pipelines.
Knowledge of security, reliability, and performance engineering best practices for AI platforms. *
Why Join Us
This is an opportunity to play a pivotal leadership role in shaping the AI operational backbone for Investments & Wealth. You will help define how AI solutions are reliably brought into production, managed, and scaled, while influencing enterprise-wide standards and advancing innovation in a critical growth area.
BNY assesses market data to ensure a competitive compensation package for our employees. The base salary for this position is expected to be between $147,000 and $240,000 per year at the commencement of employment. However, base salary if hired will be determined on an individualized basis, including as to experience and market location, and is only part of the BNY total compensation package, which, depending on the position, may also include commission earnings, discretionary bonuses, short and long-term incentive packages, and company- sponsored benefit programs.
This position is at-will and the Company reserves the right to modify base (as well as any other discretionary payment or compensation) at any time, including for reasons related to individual performance, change in geographic location, Company or individual department/team.
Salary Context
This $147K-$240K range is above the median for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At BNY, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($193K) sits 12% below the category median. Disclosed range: $147K to $240K.
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 Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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
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