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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 and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths \- whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in\-office culture that supports collaboration, engagement, and career development. Our approach includes clear in\-office expectations, while providing an appropriate level of flexibility based on role\-specific responsibilities and business needs.
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 building and leading an organization to deliver technology products and services that meet strategic business outcomes. Key responsibilities include enabling the portfolio strategy by setting strategic technology outcomes, creating a resource and people development strategy, managing the financial and resource plan, advocating and advancing organizational agility, improving end\-to\-end processes, and promoting learning and improvement. Job expectations may include coaching, mentoring, and driving leadership succession planning and development of employees.
We are seeking a highly accomplished Agentic AI Platform Engineering \& Performance Distinguished level to define and drive the architecture, engineering standards, and technical roadmap for high\-performing, resilient agentic systems embedded across the Software Delivery Lifecycle (SDLC). This role will shape how engineering teams design, measure, optimize, and scale agent runtime platforms to deliver reliable, responsive, and cost\-effective AI capabilities across developer workflows.
This role requires deep technical leadership in agent runtime and latency profiling, model performance monitoring and routing, caching, streaming, orchestration, load testing, and site reliability engineering (SRE). The ideal candidate brings strong hands\-on engineering depth, platform design expertise, and a track record of building scalable performance frameworks that improve responsiveness, throughput, resiliency, observability, and operational efficiency for agentic systems in production.
Developer Experience (DevEx) provides enterprise technical standards and common technical services, platforms, and tools that are leveraged by delivery teams across all lines of business. Within the SDLC Software Delivery Lifecycle program, this role leads engineering direction for agent runtime performance, production scalability, and operational reliability capabilities that improve how AI\-enabled delivery systems execute, respond, and perform across software delivery workflows.
Responsibilities:
- Manages relationships with business and technology executives/sponsors and vendors for technical products and understands the work deliverables and performance of the Technology Management team by following outcomes from business and client feedback
- Creates an inclusive and healthy working environment and helps to resolve organizational impediments/blockers by sponsoring opportunities that improve processes, staying close to teams and their experiences, and identifying new opportunities to enhance efficiency and gain a competitive advantage
- Conducts portfolio level resourcing and financial management activities, establishes target outcomes through objectives and key results, tracks strategic technology outcomes aligned to the portfolio strategy, and promotes the organization and its resources to achieve them
- Facilitates performance and career development of teams through performance reviews, coaching, and creating development plans that are needed to build competencies and skills
- Establishes a culture of risk and compliance management, ensuring the organization is compliant with all applicable policies and standards
- Ensures that execution is aligned with Portfolio strategy by working with Portfolio Product Executives and other value stream stakeholders
- Manages procurement, vendor evaluations, and negotiating vendor contracts for technical products and services for the organization
- Improve the experience for our developers, making it easier to deliver industry\-leading solutions, while managing work efficiently and with the right controls
- Advance our technology platforms through innovation
- Reduce risk and improve quality across our technology portfolio by aligning to a single enterprise architecture strategy and delivering governance that enables consistency, integration and automation
Required Qualification:
Engineering Leadership \& Enterprise Platforms
- 15\+ years of engineering leadership across enterprise platforms, developer tooling, or AI\-enabled systems
- Proven ability to define architecture, standards, and technical strategy, influencing senior leaders and operating effectively in regulated environments
Agent Runtime Performance, Profiling \& Optimization
- Deep expertise in runtime profiling and latency optimization, including bottleneck analysis across orchestration, models, tools, and dependencies
- Strong ability to implement performance strategies (caching, batching, async processing, streaming) to improve throughput and reduce cost
- Experience designing efficient orchestration patterns that balance execution quality, scalability, and failure isolation
Model Performance Monitoring, Routing \& Production Controls
- Experience building model performance monitoring systems (latency, throughput, errors, cost, and fallback behavior)
- Strong understanding of model routing and runtime controls to optimize quality, resiliency, and cost across varying workloads
Load Testing, Reliability Engineering \& Resilience
- Strong foundation in SRE practices (SLOs, error budgets, incident response, capacity planning, production readiness)
- Proven ability to design for resilience (load testing, graceful degradation, retries, timeouts, and failure containment)
Operational Excellence, Scale \& Engineering Impact
- Track record scaling high\-performance platforms to enterprise adoption, improving latency, reliability, cost efficiency, and developer experience
Desired Qualification:
- Advanced degree in a technical discipline or equivalent record of distinguished technical leadership in agent runtime performance, platform reliability engineering, model operations, or AI\-enabled software delivery engineering
Skills:
- Business Acumen
- Financial Management
- Influence
- Result Orientation
- Risk Management
- Analytical Thinking
- Collaboration
- Data Management
- Stakeholder Management
- Technical Strategy Development
- Architecture
- DevOps Practices
- Solution Delivery Process
- Solution Design
- User Experience Design
Shift:
1st shift (United States of America)Hours Per Week:
40
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 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 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Bank of America AI Hiring
Bank of America has 6 open AI roles right now. They're hiring across Data Engineer, AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Addison, TX, US, Charlotte, NC, US. Compensation range: $182K - $230K.
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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