Software Engineer III-Generative AI Platform Engineering

Stryker Corporation

Addison (TX)

On-site

Confidential

Full time

14 days+

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Job summary

Bank of America is seeking an experienced software engineer to build enterprise‑grade GenAI and AI platform capabilities. You will design, develop, and deliver reusable platform services, APIs, and components that support AI model development, deployment, and governance.

As an individual contributor and mentor, you will drive CI/CD practices, automated testing, and observability, while collaborating with data scientists and business stakeholders to deliver new capabilities across the AI

Qualifications

6+ years of software engineering experience with strong Python
Experience developing AI/ML, Data Science, Data Engineering, or analytics apps in enterprise
Strong understanding of GenAI and Data Science platform architectures
Hands-on experience with GenAI frameworks and tools
Experience building scalable REST APIs and microservices with FastAPI or similar
Experience with vector stores, inference services, and AI orchestration
Strong Python for production-grade apps
Experience with ML lifecycle management (MLFlow, Kubeflow)

Responsibilities

Codes solutions and unit tests to deliver stories per criteria
Designs architecture components, interfaces, and solution enablers
Mentors engineers on CI/CD practices and automating tool stacks
Executes story refinement and estimates work
Performs spike/poc to mitigate risk or try new ideas
Develops scalable APIs, microservices, and platform components for AI lifecycle
Builds agentic AI applications, AI assistants, and workflow automation with Kafka
Contributes to CI/CD pipelines, automation, testing strategies, and DevOps
Collaborates with platform engineers, data scientists, and stakeholders to deliver capabilities
Ensures security, scalability, governance, resiliency, and observability

Skills

Python

GenAI

AI Platform

REST APIs

Microservices

Kafka

Kubernetes

CI/CD

DevOps

Security & Governance

Education

Bachelor's degree in Computer Science / Engineering / Data Science or related field

Tools

FastAPI

MLFlow

Kubeflow

Kafka

Kubernetes

Git

CI/CD pipelines

Containerization

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.

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