Software Engineer III – Generative AI Platform Engineering

Bank of America; Town of Vermont (WI); On-site; USD 140,000 - 200,000; Full time

Job summary

Bank of America is seeking a hands-on software engineer to build enterprise-grade Generative AI, Data Science, and AI Platform capabilities within its AI ecosystem. You will design, develop, and deliver reusable services, APIs, and components to support model development, deployment, inferencing, and governance.

You will partner with senior engineers, data scientists, and product owners to create secure, scalable, and resilient solutions using modern AI frameworks, cloud-native tech, and

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.
• 6+ years of software engineering experience with Python.
• Experience developing AI/ML/Data Science apps in enterprise environments.
• Strong understanding of GenAI and Data Science platform architectures.
• Hands-on with AI/ML and GenAI solutions using modern frameworks.
• Experience building scalable REST APIs and microservices.
• Experience with vector stores, inference services, and AI orchestration.
• Strong Python production-grade development and reusable libraries.
• Experience with ML lifecycle frameworks like MLFlow, Kubeflow.
• API Gateway, JWT security controls.
• Understanding metadata governance, data lineage, governance principles.

Responsibilities

• Code solutions and unit tests per acceptance criteria.
• Designs, develops, and modifies architecture components and interfaces.
• Mentors engineers and coaches CI/CD practices.
• Performs spike/POCs to mitigate risk.
• Automates release activities.
• Develops AI platform capabilities and reusable services.
• Build scalable APIs and microservices for AI lifecycle.
• Ensure security, governance, and operational excellence.
• Collaborate with stakeholders to deliver new capabilities.

Skills

Python

REST APIs

CI/CD

Cloud-native

Kubernetes

Education

Bachelor’s degree in Computer Science or related field

Tools

FastAPI

Kafka

MLFlow

Kubeflow

Git

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.

Position Summary

This is a hands-on software engineering role focused on building enterprise-grade Generative AI, Data Science, and AI Platform capabilities within Bank of America’s strategic AI ecosystem. The engineer will work as an individual contributor responsible for designing, developing, and delivering reusable GenAI platform services, frameworks, APIs, and application components that support AI model development, deployment, inferencing, automation, and governance. The successful candidate will partner with senior engineers, architects, product owners, and data scientists to develop scalable, secure, and resilient solutions leveraging modern AI frameworks, cloud-native technologies, distributed computing platforms, and enterprise engineering practices. This role is ideal for an engineer passionate about Generative AI, application development, platform engineering, automation, and building reusable capabilities that accelerate enterprise AI adoption. This job is responsible for developing and delivering complex requirements to accomplish business goals.

Key responsibilities

• Ensure that software is developed to meet functional, non-functional and compliance requirements, and solutions are well designed with maintainability/ease of integration and testing built-in from the outset.
• Job expectations include a strong knowledge of development and testing practices common to the industry and design and architectural patterns.

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)
• Develop and enhance enterprise Generative AI platform capabilities, reusable services, and self-service tools.
• Design and build AI-powered applications, agentic workflows, RAG solutions, and MCP-enabled services.
• Develop scalable APIs, microservices, and platform components supporting AI/ML lifecycle management.
• Build and maintain frameworks supporting model development, fine-tuning, deployment, inferencing, monitoring and observability.
• Implement event-driven and streaming solutions leveraging technologies such as Kafka and distributed processing platforms.
• Contribute to CI/CD pipelines, automation frameworks, testing strategies, and DevOps practices.
• Collaborate with platform engineers, architects, data scientists, and business stakeholders to deliver new capabilities.
• Participate in design discussions, code reviews, sprint planning, story refinement, and estimation activities.
• Ensure solutions meet enterprise standards for security, scalability, governance, resiliency, and operational excellence.
• Support platform observability, monitoring, and performance optimization initiatives.
• Continuously evaluate emerging AI technologies and contribute innovative solutions to enhance platform capabilities.

Core Engineering Responsibilities

• Develop code and automated tests to deliver stories and requirements meeting quality and compliance standards.
• Participate in application design leveraging data, application, integration, and platform architecture patterns.
• Collaborate in requirement analysis, story refinement, and solution design activities.
• Estimate and deliver assigned work within Agile development cycles.
• Build agentic applications, AI assistants, workflow automation capabilities, and event-driven services using Kafka, containers, and MCP architectures.
• Deliver secure, scalable, observable, and resilient software solutions aligned with enterprise standards.
• Troubleshoot, optimise, and maintain platform services to ensure operational excellence.

Required Qualifications

• Bachelor’s computer science, Engineering, Data Science, or job related field required.
• 6+ years of software engineering experience with strong expertise in Python-based application development.
• Experience developing AI/ML, Data Science, Data Engineering, or analytics applications in enterprise environments.
• Strong understanding of modern Generative AI and Data Science platform architectures, including compute-storage separation, virtual environments, containers, Jupyter, and VS Code-based development.
• Hands-on experience developing AI/ML and GenAI solutions using modern frameworks and tools.
• Experience building scalable REST APIs and microservices using FastAPI or similar frameworks.
• Experience developing applications leveraging vector stores, inference services, model-serving technologies, and AI orchestration frameworks.
• Strong Python programming skills with experience building production-grade applications and reusable libraries.
• Experience with AI/ML lifecycle management frameworks such as MLFlow, Kubeflow, model deployment, fine-tuning, and inference frameworks.
• Experience building applications with API Gateway integration, JWT-based authentication, and enterprise security controls.
• Understanding of metadata management, data lineage, governance principles, and semantic layer concepts.
• Experience working within large-scale engineering organizations utilizing Git-based development, CI/CD pipelines, automated testing, and collaborative development practices.
• Familiarity with cloud-native development, containers, Kubernetes, and distributed computing environments.

Desired Qualifications

• Experience developing Retrieval-Augmented Generation (RAG) solutions.
• Experience building MCP servers, AI agents, and multi-agent orchestration frameworks.
• Knowledge of LLM integration, prompt engineering, model evaluation, and AI observability.
• Familiarity with enterprise AI governance, responsible AI, metadata, and data quality concepts.
• Exposure to enterprise-scale Generative AI platforms and self-service developer ecosystems.

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 Shift:

1st shift (United States of America) Hours Per Week: 40

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Bank of America Culture and Values

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day. One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial wellbeing. Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faced approach for flexibility, depending on the various roles in our organization. Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference.

Partnering Locally

Learn about some of the ways Bank of America is making a difference in the communities we serve.

Global Impact

Learn about the six areas that guide Bank of America’s efforts to help make financial lives better for customers, clients, communities and our teammates.

Opportunity and Inclusion

Each employee brings unique skills, background and opinions. We see opportunity and inclusion as our platform for innovation and a key component in our success.

Our Values

Learn about our four values that represent what we believe.

Pay Transparency: https://careers.bankofamerica.com/en-us/pay-transparency