Software Engineer III-Generative AI Platform Engineering

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.

- 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.

- 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.

- 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.