Software Engineer III – Generative AI Platform Engineering

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, inference, 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.

Responsibilities

• Codes solutions and unit tests 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 coaches the team on Continuous Integration and Continuous Development (CI‑CD) practices and automating the tool stack
• Executes story refinement, definition of requirements, and estimates 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)
• Develops and enhances enterprise Generative AI platform capabilities, reusable services, and self‑service tools
• Designs and builds AI‑powered applications, agentic workflows, RAG solutions, and MCP‑enabled services
• Develops scalable APIs, microservices, and platform components supporting AI/ML lifecycle management
• Builds and maintains frameworks supporting model development, fine‑tuning, deployment, inference, monitoring, and observability
• Implements event‑driven and streaming solutions leveraging technologies such as Kafka and distributed processing platforms
• Contributes to CI/CD pipelines, automation frameworks, testing strategies, and DevOps practices
• Collaborates with platform engineers, architects, data scientists, and business stakeholders to deliver new capabilities
• Participates in design discussions, code reviews, sprint planning, story refinement, and estimation activities
• Ensures solutions meet enterprise standards for security, scalability, governance, resiliency, and operational excellence
• Supports platform observability, monitoring, and performance optimization initiatives
• Continuously evaluates emerging AI technologies and contributes innovative solutions to enhance platform capabilities

Core Engineering Responsibilities

• Develops code and automated tests to deliver stories and requirements meeting quality and compliance standards
• Participates in application design leveraging data, application, integration, and platform architecture patterns
• Collaborates in requirement analysis, story refinement, and solution design activities
• Estimates and delivers assigned work within Agile development cycles
• Builds agentic applications, AI assistants, workflow automation capabilities, and event‑driven services using Kafka, containers, and MCP architectures
• Delivers secure, scalable, observable, and resilient software solutions aligned with enterprise standards
• Troubleshoots, optimizes, and maintains platform services to ensure operational excellence

Required Qualifications

• Bachelor’s degree in computer science, engineering, data science, or a related field
• 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