Full Stack Engineer - AI Applications

First Solar (US)•Perrysburg, OH

12d•$150,880 - $215,050•Onsite

About The Position

First Solar reserves the right to offer you a role most applicable to your experience and skillset. This role involves designing, developing, and maintaining full stack AI-powered web applications to support the organization’s digital transformation and product engineering initiatives. You will build and deploy production-grade applications using React/TypeScript frontends and Python/FastAPI backends on Azure cloud infrastructure. The position requires architecting scalable, containerized solutions and establishing reusable design patterns across multiple AI product workstreams. Collaboration with data scientists and stakeholders to integrate LLMs, AI agents, and machine learning models into business applications is key. You will also design and implement data engineering pipelines, oversee architectural decisions, CI/CD pipelines, and infrastructure-as-code. Additionally, you will provide technical leadership by researching best practices, setting engineering standards, and mentoring team members.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related technical field is required.
Minimum of 5 years of professional experience in full stack software development with modern web frameworks and cloud-native architectures is required.
Strong proficiency in React with TypeScript and modern frontend tooling (Vite, Tailwind CSS).
Advanced backend development skills in Python (FastAPI), with the ability to design and build RESTful APIs and asynchronous services.
Hands-on experience with Azure cloud services including Container Apps, Blob Storage, Cognitive Search, Key Vault, Azure AI Foundry, Azure OpenAI, Azure Identity, and Azure Data Factory or equivalent data integration services.
Proficiency in containerization and orchestration with Docker and Kubernetes, and experience building CI/CD pipelines with Azure DevOps or Azure Pipelines.
Experience with Infrastructure as Code (Terraform) for provisioning and managing cloud resources.
Strong knowledge of SQL databases (Microsoft SQL Server preferred), ORM frameworks (SQLAlchemy), and database migration tools (Alembic).
Data engineering skills including designing ETL/ELT pipelines, data modeling, data ingestion from heterogeneous sources (SQL, Excel, flat files, APIs), and data quality validation.
Demonstrated experience integrating AI/ML models and large language models (LLMs) into production-grade applications.
Solid understanding of application security best practices including OWASP principles, input validation, secrets management, and RBAC/OAuth/OIDC authentication and authorization patterns.
Experience with testing frameworks and methodologies including unit testing, integration testing, and test automation (Pytest, Jest, React Testing Library).
Knowledge of caching strategies (Redis), query optimization, and application performance tuning for high-availability systems.
Experience with monitoring and observability tools such as Azure Monitor, Application Insights, structured logging, and alerting frameworks.
Familiarity with event-driven and message-driven architectures (Azure Service Bus, Event Grid) for asynchronous processing and agent-based workloads.
Solid understanding of software development lifecycle (SDLC) best practices, version control (Git), and agile methodologies.
Ability to make sound architectural decisions, establish coding standards, and create reusable design patterns for scalable systems.
Experience with data visualization libraries (Recharts, Plotly, Matplotlib) and data processing frameworks (Pandas, NumPy).
Strong written and verbal communication skills with the ability to collaborate effectively across cross-functional teams and translate business requirements from non-technical stakeholders into technical solutions.
Demonstrated technical leadership skills with the ability to research, evaluate, and adopt industry-standard engineering practices and mentor peers in their application.
Proficient use of all Microsoft Office suite programs.

Nice To Haves

Master’s degree in Computer Science, Software Engineering, Data Engineering, or a related discipline is preferred.
Experience with AI/ML integration, LLM-based application development, Azure cloud services, and DevOps practices is highly preferred.
Experience designing and building ETL/ELT data pipelines, data ingestion workflows, or data platform architectures is preferred.

Responsibilities

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/TypeScript frontends and Python/FastAPI backends on Azure cloud infrastructure.
Architect scalable, containerized solutions and establish reusable design patterns across multiple AI product workstreams 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/TypeScript 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 DevOps 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 DevOps.
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 ongoing maintenance.
Other duties as assigned.