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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!
Job Description:
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.
- Lead design, development, and deployment of AI and non\-AI applications across multiple business domains
- Own delivery accountability across planning, execution, testing, and production rollout
- Ensure alignment with enterprise architecture, security, and compliance standards
- Design and implement AI\-powered solutions, including: Large Language Models (LLMs), prompt engineering, Retrieval\-Augmented Generation (RAG) pipelines, Agent\-based architectures and orchestration frameworks
- Integrate AI capabilities into enterprise systems via APIs and microservices
- Evaluate and adopt emerging AI technologies (e.g., Copilot, Foundry, open\-source frameworks)
- Develop scalable backend services using: Java (Spring Boot, Microservices architecture), Python (FastAPI, data pipelines, AI/ML frameworks)
- Build and optimize high\-performance, resilient, and maintainable systems
- Ensure best practices in coding standards, testing, and code reviews
- Define solution architectures for complex systems involving: Distributed systems and microservices, Event\-driven architectures, Cloud\-native patterns (Azure/AWS/GCP)
- Ensure system observability (logging, monitoring, alerting)
- Drive production support readiness, including incident resolution and RCA
- Provide technical guidance to engineering teams
- Conduct design reviews and mentor junior/mid\-level engineers
- Promote engineering best practices and continuous learning
- Partner with product owners, architects, and business stakeholders to translate requirements into technical solutions
- Communicate complex technical concepts to both technical and non\-technical audiences
- Contribute to strategic initiatives and roadmap planning
Responsibilities:
- Lead end\-to\-end delivery of AI and non\-AI software solutions across multiple projects
- Design and implement scalable architectures (microservices, cloud\-native, event\-driven)
- Build and maintain backend systems using Java and Python
- Develop and integrate AI solutions (LLMs, RAG, agents, ML pipelines) into enterprise platforms
- Translate business requirements into technical designs and high\-quality implementations
- Ensure adherence to enterprise standards for security, compliance, and performance
- Drive code quality, testing, and engineering best practices across teams
- Implement and manage CI/CD pipelines, monitoring, and production readiness
- Provide technical leadership and mentorship to engineers and review solution designs
- Collaborate with stakeholders to align technology delivery with business goals
- Create and review technical design documents, architecture diagrams, and standards
- Drive reusability, modularity, and scalability across solutions
- Design and manage data ingestion, transformation, and processing pipelines
- Work with structured and unstructured data, including financial and operational datasets
- Implement feature engineering, model deployment, and monitoring pipelines
Required Qualifications:
- 10\+ years of experience in software engineering and system design
- Proven experience delivering large\-scale enterprise applications and AI solutions
- Strong expertise in: Java (Spring Boot, Microservices), Python (AI/ML, APIs, data engineering)
- Hands\-on experience with:
- AI/ML frameworks (OpenAI, Hugging Face, LangChain,etc.)
- RAG pipelines, embeddings, vector databases
- RESTful APIs, distributed systems
- Deep understanding of:
- Microservices, APIs, event\-driven architectures
- Cloud platforms (Azure preferred)
- Containerization (Docker, Kubernetes)
- Practical experience with:
- LLM\-based applications and prompt engineering
- Model lifecycle management (training, deployment, monitoring)
- AI governance, risk, and explainability (preferred in regulated industries)
- Strong problem\-solving and analytical thinking
- Excellent communication and stakeholder management
- Ability to operate in a fast\-paced, ambiguous environment
Desired Qualifications:
- Experience in financial services or regulated industries
- Exposure to Microsoft ecosystem (Copilot Studio, Foundry, Fabric)
- Familiarity with agent orchestration, MCP, and AI platform integration patterns
- Experience with data privacy, compliance, and secure AI deployments
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
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 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Bank of America, 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 Required
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 $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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 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 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).
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 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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