Full Stack Engineer - AI

Design, develop, and maintain full stack AI-powered web applications that support the organization’s digital transformation and product engineering initiatives.

• Build and deploy production-grade applications using React/Type Script frontends and Python/FastAPI backends on Azure cloud infrastructure.
• Architect scalable, containerized solutions and establish reusable design patterns across multiple AI product work streams spanning analytics, reliability engineering, technical sales, and field operations.
• Collaborate with data scientists and cross-functional stakeholders to integrate large language models (LLMs), AI agents, and machine learning models into bespoke business applications.
• Design and implement data engineering pipelines to ingest, transform, and serve data from heterogeneous sources including SQL databases and third-party APIs.
• Oversee architectural decisions, CI/CD pipelines, and infrastructure-as-code to ensure reliability, security, and maintainability across the software development lifecycle.
• Provide technical leadership by researching and adopting industry-standard best practices, setting engineering standards, and mentoring team members on software engineering principles.
• Design, develop, test, and maintain full stack web applications using React/Type Script frontends and Python/FastAPI backends to deliver AI-powered tools for internal business units.
• Architect and implement scalable, cloud-native solutions on Azure including Container Apps, Kubernetes, Virtual Networks, Azure Container Registry, and Private Endpoints.
• Integrate large language models (LLMs), AI agents, and machine learning models into production applications to support business analytics, predictive modeling, and automated decision-making.
• Design, build, and maintain ETL/ELT data pipelines to ingest, transform, and serve data from heterogeneous sources including SQL databases and external APIs.
• Build and maintain CI/CD pipelines using Azure Dev Ops for automated testing, container image builds, and deployment workflows.
• Develop and manage Infrastructure as Code (IaC) using Terraform to provision, configure, and maintain Azure cloud resources.
• Create data-driven dashboards, visualization tools, and analytics platforms for performance prediction, quality engineering, and field operations use cases.
• Implement application security best practices including secure coding standards, vulnerability remediation, secrets management, and access control design across all applications.
• Establish and enforce coding standards, reusable component libraries, and architectural best practices across all product engineering projects.
• Implement monitoring, observability, and alerting solutions using Azure Monitor and Application Insights to ensure production reliability and performance.
• Collaborate with data scientists to operationalize machine learning models, integrate data pipelines, and translate analytical prototypes into production-ready applications.
• Engage directly with non‑technical business stakeholders across Performance and Prediction Analytics, Quality and Reliability Engineering, Global Quality, Technical Sales, Environment Health and Safety, Recycling, and Warranty and Post Sales to gather requirements and deliver tailored solutions.
• Provide technical leadership and mentorship to team members including data scientists and future engineering hires, setting industry‑standard best practices for code quality, architecture, testing, and Dev Ops.
• Participate in code reviews, technical design discussions, and documentation to promote knowledge sharing and engineering excellence.
• Manage the full software development lifecycle from requirements gathering and technical design through development, testing, deployment, and…