Software Engineer III-Generative AI Platform Engineering

This hands-on software engineering role concentrates on delivering enterprise-grade Generative AI, data science, and AI platform capabilities within the bank's AI ecosystem. The position focuses on designing, developing, and delivering reusable GenAI platform services, frameworks, APIs, and application components that support model development, deployment, inference, automation, and governance. This role is based onsite in Addison, TX.

Responsibilities

• Develop code and unit tests to satisfy a defined story, acceptance criteria, and compliance requirements.
• Design, evolve, and adjust architecture components, interfaces, and solution enablers while preserving architectural integrity.
• Mentor other engineers and guide the team on CI/CD practices and automation of the tool stack.
• Lead story refinement, requirements definition, and effort estimation to deliver work through the lifecycle.
• Perform spikes or proofs of concept as needed to mitigate risk or explore new ideas.
• Automate manual release activities to streamline deployment processes.
• Create and maintain automated test suites, including integration, regression, and performance tests.
• 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 the AI/ML lifecycle.
• Build and maintain frameworks for model development, fine-tuning, deployment, inference, monitoring, and observability.
• Implement event-driven and streaming solutions using 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 extend platform capabilities.
• Develop code and automated tests to deliver stories meeting quality and compliance standards.
• Engage in application design leveraging data, 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 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.
• Minimum of 6 years in software engineering with strong Python application development experience.
• Experience building AI/ML, data science, data engineering, or analytics applications in enterprise environments.
• Solid understanding of modern GenAI and data science platform architectures, including compute-storage separation, virtual environments, containers, Jupyter, and VS Code development.
• Hands-on experience delivering AI/ML and GenAI solutions with current frameworks and tools.
• Proven ability to build scalable REST APIs and microservices using FastAPI or comparable frameworks.
• Experience developing applications that leverage vector stores, inference services, model-serving technologies, and AI orchestration frameworks.
• Strong Python programming skills with a track record of 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 operating within 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
• Containers
• Kubernetes
• Jupyter
• VS Code
• MLFlow
• Kubeflow
• API Gateway
• JWT
• Vector stores
• Model-serving technologies
• AI orchestration frameworks
• Git
• MCP
• REST APIs

Shift

1st shift (United States of America)

Hours per Week

40