Software Engineer III – Generative AI Platform Engineering

Position Summary

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 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