Software Engineer III-Generative AI Platform Engineering

Onsite role in Addison, TX focused on building enterprise-grade Generative AI, Data Science, and AI Platform capabilities within Bank of America's AI ecosystem.

Responsibilities

• Write code and unit tests to deliver a defined requirement or story per acceptance criteria and compliance needs
• Architect, develop, and modify architecture components, APIs, and solution enablers while preserving architecture integrity
• Mentor software engineers and guide the team on CI/CD practices and automating the toolchain
• Lead story refinement, define requirements, and estimate work needed to deliver a story through the lifecycle
• Perform spikes or proofs of concept 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 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/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
• Participate in design discussions, code reviews, sprint planning, story refinement, and estimation
• Ensure solutions meet enterprise standards for security, scalability, governance, resiliency, and operational excellence
• Support platform observability, monitoring, and performance optimization efforts
• Continuously evaluate emerging AI technologies and contribute innovative solutions to enhance platform capabilities
• 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, optimize, and maintain platform services to ensure operational excellence

Requirements

• Bachelor’s degree in computer science, engineering, data science, or a related field is required
• 6+ years of software engineering experience with strong 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 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 employing 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
• containers
• Kubernetes
• Jupyter
• VS Code
• MLFlow
• Kubeflow
• API Gateway
• JWT
• vector stores
• model-serving technologies
• AI orchestration frameworks
• Git
• MCP
• REST APIs

Schedule

• Shift: 1st shift (United States of America)
• Hours per week: 40