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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 developing and delivering complex requirements to accomplish business goals. Key responsibilities of the job include ensuring that software is developed to meet functional, non\-functional and compliance requirements, coding solutions, unit testing, and ensuring the solution can be integrated successfully into the overall application/system with clear, robust, and well\-tested interfaces. Job expectations include an awareness of development and testing practices in the industry.
LOB Position Summary:
This position is part of Bank of America's Erica, Chat, and Voice Technology organization, the team behind one of the bank's most strategic and visible AI\-powered platforms. Erica serves millions of clients across digital channels and is at the forefront of transforming how customers interact with Bank of America through conversational AI, intelligent search, personalized insights, and virtual assistance.
As part of the Erica engineering team, you will help build the next generation of AI\-driven experiences, leveraging cutting\-edge technologies including Generative AI, Large Language Models (LLMs), Retrieval\-Augmented Generation (RAG), intelligent search, agent\-based architectures, and enterprise\-scale AI platforms. This role provides a unique opportunity to work on high\-impact initiatives that directly influence client engagement, digital adoption, and the future of AI\-powered banking.
You will collaborate with experienced engineers, architects, data scientists, and product leaders to develop scalable, secure, and resilient solutions that operate at enterprise scale. The work spans AI platform engineering, conversational experiences, intelligent orchestration, search technologies, and modern cloud\-native software development.
This is an excellent opportunity for engineers who are passionate about innovation, interested in solving complex technical challenges, and excited to contribute to one of the financial industry's leading AI and digital transformation programs.
Role Summary
The candidate will design, develop, test, and support scalable software platforms and AI\-powered applications that enable the future of conversational banking and personalized client experiences.
The role combines strong software engineering fundamentals with modern AI application development, offering hands\-on experience building enterprise solutions powered by Generative AI, LLMs, intelligent search, and machine learning technologies.
Engineers in this role will contribute to critical initiatives that help shape the evolution of Erica and Bank of America's next\-generation digital experiences.
The successful candidate will:
Work on one of the banking industry's most recognized AI\-powered virtual assistants.
Help shape the future of AI and digital experiences for millions of Bank of America clients.
Contribute to strategic initiatives involving Generative AI, LLMs, RAG, intelligent search, and conversational platforms.
Partner with industry\-leading engineers, architects, data scientists, and AI specialists.
Gain exposure to modern software engineering practices, cloud\-native architectures, and enterprise\-scale AI systems.
Tackle challenging technical problems with direct business impact and executive visibility.
Responsibilities:
- Codes solutions and unit test to deliver a requirement/story per the defined acceptance criteria and compliance requirements
- Designs, develops, and modifies architecture components, application interfaces, and solution enablers while ensuring principal architecture integrity is maintained
- Mentors other software engineers and coach team on Continuous Integration and Continuous Development (CI\-CD) practices and automating tool stack
- Executes story refinement, definition of requirements, and estimating work necessary to realize a story through the delivery lifecycle
- Performs spike/proof of concept as necessary to mitigate risk or implement new ideas
- Automates manual release activities
- Designs, develops, and maintains automated test suites (integration, regression, performance)
- Implement high\-quality code following enterprise standards, architecture patterns, and engineering best practices.
- Participate in solution design discussions and contribute to technical implementation decisions.
- Develop and integrate AI/ML\-powered capabilities into enterprise applications.
- Support implementation of Generative AI and Retrieval Augmented Generation (RAG) solutions.
- Develop and maintain RESTful APIs, microservices, and backend services.
- Perform code reviews and contribute to continuous improvement of engineering practices.
- Troubleshoot, debug, and resolve application and production issues.
- Ensure solutions meet security, performance, scalability, and reliability requirements.
- Collaborate with product owners, architects, and engineering teams to deliver business requirements.
- Support deployment, monitoring, and optimization of applications across enterprise environments.
- Participate in Agile ceremonies including sprint planning, estimation, and retrospectives.
Required Qualifications:
- 1\+ years of software development experience using Java, Python, or related technologies.
- Strong object\-oriented programming and software design skills.
- Experience building enterprise applications and RESTful web services.
- Knowledge of AI/ML concepts and experience developing AI\-enabled applications.
- Familiarity with Large Language Models (LLMs), Generative AI, and modern AI frameworks.
- Experience implementing Retrieval Augmented Generation (RAG) solutions and retrieval techniques.
- Experience working with source control tools such as Git.
- Strong analytical and problem\-solving skills.
- Good verbal and written communication skills.
- Experience working in Agile development environments.
Desired Qualifications:
- Experience with Elasticsearch, OpenSearch, SOLR, or similar search platforms.
- Experience with AI/ML frameworks and model integration.
- Experience with vector databases, embeddings, and semantic search.
- Experience with Kubernetes, Docker, and containerized deployments.
- Experience with cloud platforms such as Azure or AWS.
- Experience with Cassandra, Redis, or other distributed data technologies.
- Understanding of MLOps, model deployment, monitoring, and observability concepts.
- Experience with chatbot, conversational AI, or virtual assistant platforms.
- Experience with prompt engineering and model evaluation techniques.
- Experience with GPU\-based AI workloads and inference platforms.
Skills:
- Application Development
- Automation
- Influence
- Solution Design
- Technical Strategy Development
- Architecture
- Business Acumen
- DevOps Practices
- Result Orientation
- Solution Delivery Process
- Analytical Thinking
- Collaboration
- Data Management
- Risk Management
- Test Engineering
Minimum Education Requirement: Bachelor's or Master's Degree in Computer Science, Engineering, Information Systems, or a related field or equivalent experience
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 4,317 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 $218,500 based on 729 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
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 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 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).
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 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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