Bank of America offers benefits that are affordable, competitive, and flexible, designed to support your growth and well being. This onsite role in Addison, Texas focuses on building enterprise grade Generative AI, data science, and AI platform capabilities. You will contribute as an individual contributor, delivering GenAI platform services, APIs, and components while collaborating with engineers, data scientists, and stakeholders.
Position Summary
In this hands on software engineering role, you will design, develop, and maintain GenAI, data science, and AI platform capabilities within the bank’s AI ecosystem. You will deliver reusable platform services, frameworks, APIs, and application components that support model development, deployment, inference, automation, and governance. You will work with senior engineers, architects, product owners, and data scientists to create scalable, secure, and resilient solutions using modern AI frameworks, cloud native technologies, distributed computing, and enterprise engineering practices.
Benefits
Affordable, competitive, and flexible benefits designed to support career progression and work life balance.
Responsibilities
- Implement code and unit tests to satisfy stories while meeting acceptance criteria and compliance requirements.
- Design, develop, and adjust architecture components, interfaces, and solution enablers while preserving core architectural integrity.
- Mentor software engineers and promote CI/CD practices across the team, including automating toolchains.
- Lead story refinement, clarify requirements, and estimate work for delivery along the lifecycle.
- Execute spike or proof of concept activities to mitigate risk or explore new ideas.
- Automate manual release activities to streamline deployment processes.
- Design, develop, and maintain automated test suites for integration, regression, and performance testing.
- Develop and enhance enterprise GenAI platform capabilities, reusable services, and self service tooling.
- Design and build AI powered applications, agentic workflows, RAG solutions, and MCP enabled services.
- Develop scalable APIs, microservices, and platform components that support AI and ML lifecycle management.
- Build and maintain frameworks for model development, fine tuning, deployment, inference, monitoring, and observability.
- Implement event driven and streaming solutions using Kafka and related 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
- Write code and automated tests to deliver stories that meet quality and compliance standards.
- Participate in application design using data, integration, and platform architecture patterns.
- Collaborate on requirement analysis, story refinement, and solution design activities.
- Estimate and deliver assigned work within Agile development cycles.
- Build agentic applications, AI assistants, workflow automation, and event driven services using Kafka, containers, and MCP architectures.
- Deliver secure, scalable, observable, and resilient software aligned with enterprise standards.
- Troubleshoot, optimize, and maintain platform services to sustain operational excellence.
Technologies
- Python
- FastAPI
- Kafka
- Jupyter
- VS Code
- Git
- Kubernetes
- Containers
- MLFlow
- Kubeflow
- API Gateway
- JWT-based authentication
- REST APIs
- MCP
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related field
- Minimum 6 years of software engineering experience with strong Python development
- Experience building AI/ML, data science, data engineering, or analytics applications in enterprise environments
- Solid understanding of modern GenAI and data science platform architectures, including compute-storage separation, virtual environments, containers, Jupyter, and VS Code based development
- Hands on experience building AI/ML and GenAI solutions using contemporary frameworks and tools
- Experience constructing scalable REST APIs and microservices with FastAPI or similar frameworks
- Experience developing applications employing vector stores, inference services, model serving technologies, and AI orchestration frameworks
- Strong Python programming skills with 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 in large scale engineering organizations with Git based development, CI/CD pipelines, automated testing, and collaborative practices
- Familiarity with cloud native development, containers, Kubernetes, and distributed computing environments
Schedule
- Shift: 1st shift (United States of America)
- Hours per week: 40
