Interested in this AI/ML Engineer role at Bank of America?
Apply Now →Skills & Technologies
About This Role
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being an inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
Bank of America is committed to an in\-office culture with specific requirements for office\-based attendance and which allows for an appropriate level of flexibility for our teammates and businesses based on role\-specific considerations.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
This job is responsible for providing leadership, technical direction and oversight to a team delivering technology solutions. Key responsibilities of the job are to provide oversight of the design, implementation, and maintenance of complex computer programs, align technical solutions to business objectives, and ensure that coding practices/quality comply with software development standards. Job expectations include conducting multiple software implementations and applying both depth and breadth in knowledge of several technical competencies.
Job Profile Summary:
Generative AI (GenAI) presents an exciting opportunity to derive valuable insights from data and drive revenue growth, efficiencies, and improved business processes. Technology will collaborate with Global Markets Sales \& Trading, Quantitative Strategies \& Data Group (QSDG) \& Platform teams to the design and buildout its global GenAI platform.
Platform will cater to a rapidly growing number of use cases that harness the power of GenAI, leveraging both proprietary and open\-source Large Language Models, and large structured and un structured data sets to provide insights to Global Markets Sales \& Trading, QSDG, Research, and Operations.
Feature Lead Data Engineer to build out data pipelines to source large volumes of structured (ex: KDB) \& unstructured data (ex: Research documents, Term Sheets), classify, and store data to meet GenAI requirements. The role will design, develop, and engineer platform for high performance and scalability.
Responsibilities:
- Designs, develops and is accountable for feature delivery
- Applies enterprise standards for solution design, coding and quality
- Ensures solution meets product acceptance criteria with minimal technical debt
- Guides the team on work breakdown and execution
- Works with the Product Owner to ensure that product backlog/requirements are healthy, with clear acceptance criteria
- Plays a team lead role (as an individual contributor) and mentors the team
- Guides team members with skills and practices (planning and estimation, peer reviews, and other engineering practices)
- Identify, prioritize, and execute tasks in the software development life cycle
- Establish standards and lead the charge on automation for end\-to\-end SDLC efficiency
- Implement software enhancement to meet global performance expectations
- Ensures solution meets product acceptance criteria with minimal technical debt
- Guides the team on work breakdown and execution
- Works with the Product Owner to ensure that product backlog/requirements are healthy, with clear acceptance criteria
- Plays a technical lead role (as an individual contributor) and coach junior developers.
- Guides team members with skills and practices (planning and estimation, peer reviews, and other engineering practices)
Required Qualifications
- Experience in designing \& implementing multi\-tier (including microservices) application platforms (Fast API)
- Extensive experience with high performance Pub/Sub (Kafka) architecture.
- Extensive experience with Distributed System – specifically Redis, Kubernetes (K8s), Docker Containers
- High coding proficiency in Python
- In\-depth knowledge of relational databases (e.g. Oracle, MySQL) and NoSQL databases (e.g. MongoDB), and large structured \& unstructured dataset processing
- Strong communication skills to effectively collaborate with various stakeholders
- Critical thinking and problem\-solving skills are essential
Desired Qualifications
- Experience implementing data ingestion, Retrieval Augmented Generation(RAG), Agentic RAG, Agent frameworks (LangGraph).
Skills:
- Automation
- Influence
- Result Orientation
- Stakeholder Management
- Technical Strategy Development
- Architecture
- Business Acumen
- Risk Management
- Solution Delivery Process
- Solution Design
- Agile Practices
- Analytical Thinking
- Collaboration
- Data Management
- DevOps Practices
Minimum Education Requirements: Bachelor's Degree or Equivalent Professional Experience
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $106K-$173K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1616 roles with salary data).
View full AI/ML Engineer salary data →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 3,057 AI roles we're tracking, AI/ML Engineer positions make up 72% of the market. At Bank of America, 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 Required
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 $179,000 based on 11,905 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 22% below the category median. Disclosed range: $106K to $173K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Bank of America AI Hiring
Bank of America has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $173K - $173K.
Location Context
AI roles in New York pay a median of $210,000 across 2,449 tracked positions. That's 5% above the national 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 3,057 open positions tracked in our dataset. By seniority: 94 entry-level, 1,467 mid-level, 1,148 senior, and 348 leadership roles (Director, VP, C-Level). Remote roles make up 17% of the market (513 positions). The remaining 2,528 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,057 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,189), Data Scientist (233), AI Software Engineer (195). 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 (94) are outnumbered by mid-level (1,467) and senior (1,148) 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 348 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 17% of all AI roles (513 positions), with 2,528 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,566 postings), Aws (974 postings), Azure (725 postings), Rag (683 postings), Gcp (597 postings), Prompt Engineering (472 postings), Pytorch (461 postings), Claude (447 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.