Software Engineer III – Generative AI Platform Engineering

Responsibilities

• Code solutions and unit tests to deliver stories that meet acceptance criteria and compliance requirements
• Design, develop, and modify architecture components, interfaces, and solution enablers while preserving architectural integrity
• Mentor software engineers and coach the team on CI/CD practices and automating tool stacks
• Participate in story refinement, define requirements, and estimate work for delivery lifecycle
• Execute spike or proof-of-concept work to mitigate risk or explore new ideas
• Automate manual release activities
• Design, develop, and maintain automated test suites (integration, regression, performance)
• Develop and enhance enterprise GenAI 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 for model development, fine-tuning, deployment, inferencing, monitoring, and observability
• Implement event-driven and streaming solutions using 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
• Engage in design discussions, code reviews, sprint planning, story refinement, and estimation activities
• Ensure solutions meet security, scalability, governance, resiliency, and operational excellence standards
• Support platform observability, monitoring, and performance optimization initiatives
• Continuously assess emerging AI technologies and contribute innovative solutions to enhance platform capabilities

Core Engineering Responsibilities

• Develop code and automated tests to deliver stories that meet quality and compliance standards
• Contribute to application design using data, integration, and platform architecture patterns
• Collaborate on requirement analysis, story refinement, and solution design activities
• Estimate and deliver 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 aligned with enterprise standards
• Troubleshoot, optimize, and maintain platform services to achieve operational excellence

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field
• 6+ years of software engineering experience with strong Python development skills
• Experience building AI/ML, Data Science, Data Engineering, or analytics applications in enterprise settings
• Deep understanding of modern GenAI and Data Science platform architectures, including compute-storage separation, virtual environments, containers, Jupyter, and VS Code-based workflows
• Hands-on experience developing AI/ML and GenAI solutions with modern frameworks and tools
• Proven ability to build scalable REST APIs and microservices using FastAPI or similar frameworks
• Experience creating applications that leverage 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 and Kubeflow, including model deployment, fine-tuning, and inference
• 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 using Git-based development, CI/CD pipelines, automated testing, and collaborative practices
• Familiarity with cloud-native development, containers, Kubernetes, and distributed computing environments

Technologies

• Python
• FastAPI
• Kafka
• Jupyter
• VS Code
• Git
• Kubernetes
• Containers
• MLFlow
• Kubeflow
• API Gateway
• JWT-based authentication
• REST APIs
• MCP

Benefits

• affordable, competitive and flexible benefits

Position Summary

• Hands-on software engineering role focused on enterprise-grade Generative AI, Data Science, and AI Platform capabilities within Bank of America's AI ecosystem
• Individual contributor responsible for designing, developing, and delivering reusable GenAI platform services, frameworks, APIs, and components to support model development, deployment, inferencing, automation, and governance
• Collaboration with senior engineers, architects, product owners, and data scientists to create scalable, secure, and resilient solutions using modern AI frameworks, cloud-native tech, and enterprise practices
• Ideal for engineers passionate about Generative AI, platform engineering, automation, and building reusable capabilities that accelerate enterprise AI adoption
• Role emphasizes delivering solutions that meet functional, non-functional, and compliance requirements with maintainability and testability built-in from the start

Shift

1st shift (United States of America)

Hours Per Week

40