Job Description: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 of the job include ensuring 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, optimize, and maintain platform services to ensure operational excellence.
Required Qualifications- Bachelor's degree in 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
