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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 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!
Position Summary:
This is a senior platform automation engineering role focused on accelerating enterprise adoption of Generative AI, Data Science, Data Engineering, and Advanced Analytics capabilities across Bank of America. The role will lead automation initiatives that improve developer productivity, platform reliability, operational efficiency, governance, and self\-service adoption across enterprise AI and data platforms.
The successful candidate will be responsible for designing, building, and operationalizing automated platform capabilities spanning infrastructure provisioning, CI/CD, environment management, governance controls, observability, testing, deployment automation, and AI workload enablement. The individual will work closely with platform engineering, cloud engineering, architecture, data science, and business teams to deliver scalable, secure, and resilient automation solutions supporting the full lifecycle of AI and analytics workloads.
This role requires strong expertise in platform automation, cloud\-native technologies, Infrastructure\-as\-Code (IaC), DevSecOps, Generative AI ecosystem tooling, and distributed computing platforms. The ideal candidate combines deep engineering expertise with a passion for automation, operational excellence, and continuous platform innovation
This job is responsible for defining and leading the engineering approach for complex features to deliver significant business outcomes. Key responsibilities of the job include delivering complex features and technology, enabling development efficiencies, providing technical thought leadership based on conducting multiple software implementations, and applying both depth and breadth in a number of technical competencies. Additionally, this job is accountable for end\-to\-end solution design and delivery.
Responsibilities:
- Ensures that the design and engineering approach for complex features are consistent with the larger portfolio solution
- Define the technology tool stack for the solution and evaluate and adapt new testing tool/framework/practices for team(s)
- Enables team(s)/applications with Continuous Integration/Continuous Development (CI/CD) capabilities and engages with other technical stakeholders pertaining to efficient functioning of CI\-CD pipeline
- Guides and influences team(s) on design and best practices for high code performance –e.g. pairing, code reviews
- Provides end\-to\-end delivery of complex features, including automation, for either a single team or multiple teams, at the program level
- Conducts research, design prototyping and other exploration activities such as evaluating new toolsets and components for release management, CI/CD, and features
- Works with stakeholders to establish high\-level solution needs and with architects for technical requirements
- Lead automation initiatives for enterprise GenAI, Data Science, Metadata, Data Quality, Event Streaming, and Analytics platforms.
- Design and implement self\-service automation capabilities that streamline onboarding, environment provisioning, deployment, governance, monitoring, and operational workflows.
- Build automated platform services supporting the complete AI and analytics lifecycle including data preparation, experimentation, model training, deployment, inferencing, observability, and lifecycle management.
- Develop Infrastructure\-as\-Code (IaC) solutions using Terraform and related automation frameworks to enable repeatable, scalable, and compliant infrastructure deployments.
- Design and implement enterprise CI/CD pipelines, automated testing frameworks, deployment automation, and release management processes using Atlassian and related DevOps toolchains.
- Partner with platform engineering and cloud teams to automate Kubernetes, container, serverless, and distributed computing environments.
- Build automation solutions supporting agentic AI applications, MCP\-enabled services, event\-driven architectures, and enterprise AI workflows.
- Drive operational excellence through platform monitoring, observability, automated remediation, performance optimization, and reliability engineering practices.
- Collaborate with architecture, engineering, governance, security, and business stakeholders to ensure platforms meet enterprise standards and compliance requirements.
- Conduct technical design reviews, automation assessments, code reviews, and establish engineering best practices across teams.
- Provide technical leadership, mentorship, and guidance to engineering teams adopting automation\-first development and operational practices.
- Support key business initiatives including Consumer AML Analytics and other strategic AI platform adoption efforts.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, or job related field required .
- 10\+ years of hands\-on experience in platform engineering, automation engineering, cloud engineering, DevOps, or large\-scale distributed systems.
- Proven experience building self\-service enterprise platforms supporting AI/ML, Data Science, Data Engineering, and advanced analytics workloads.
- Strong expertise in automation frameworks, DevOps methodologies, CI/CD pipelines, Infrastructure\-as\-Code, and software delivery lifecycle automation.
- Deep understanding of modern open\-source Generative AI and Data Science platform architectures including storage and compute separation, interactive development environments, virtual environments, containers, Jupyter, VSCode, and developer productivity tooling.
- Hands\-on experience implementing enterprise CI/CD automation using Atlassian ecosystem tools including Bitbucket, Bamboo, Jira, and Confluence.
- Experience designing and implementing Infrastructure\-as\-Code solutions using Terraform and cloud\-native automation frameworks.
- Strong understanding of metadata management, data lineage, governance frameworks, and semantic layer concepts supporting enterprise AI and data platforms.
- Experience building scalable cloud\-native solutions utilizing distributed computing architectures and modern platform engineering principles.
- Experience automating deployments and operations for Kubernetes, containerized, YARN, serverless, and distributed processing environments.
- Experience designing and supporting event\-driven architectures leveraging technologies such as Kafka and streaming data platforms.
- Working knowledge of agentic AI architectures, MCP frameworks, API integrations, workflow automation, and enterprise AI enablement platforms.
- Strong Python development experience for automation, orchestration, scripting, tooling, and operational engineering use cases.
- Knowledge of cloud engineering principles including networking, infrastructure management, security, resilience, scalability, and cost optimization.
- Experience implementing observability frameworks including logging, monitoring, tracing, alerting, automation, and operational dashboards.
- Ability to communicate effectively with engineers, architects, product owners, and business stakeholders across varying
Desired Qualifications
- Experience supporting enterprise Generative AI platforms, AI governance frameworks, model management, and AI operationalization initiatives.
- Knowledge of AML, financial crime, risk analytics, fraud detection, or banking domain platforms.
- Experience building platform automation for data governance, data quality, metadata management, and model lifecycle management.
- Experience implementing GitOps, DevSecOps, Reliability Engineering (RE), and platform engineering best practices.
- Familiarity with large\-scale cloud environments and enterprise data platforms.
- Experience creating reusable developer platforms, internal engineering tools, and self\-service automation capabilities at enterprise scale
Skills:
- Automation
- Influence
- Result Orientation
- Stakeholder Management
- Technical Strategy Development
- Application Development
- Architecture
- Business Acumen
- Risk Management
- Solution Design
- Agile Practices
- Analytical Thinking
- Collaboration
- Data Management
- Solution Delivery Process
Shift:
1st shift (United States of America)Hours Per Week:
40
Salary Context
This $122K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($161K) sits 26% below the category median. Disclosed range: $122K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Bank of America AI Hiring
Bank of America has 8 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer. Positions span Plano, TX, US, New York, NY, US, Pennington, NJ, US. Compensation range: $200K - $232K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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