Sr. Full Stack AI Engineer

The Hartford•Columbus, OH

2d•Hybrid

About The Position

We are seeking a highly skilled Full Stack Engineer who combines deep software engineering expertise with advanced proficiency in AI-powered development tools. This role is focused on hands-on building and delivery — a practitioner who ships production-quality code end-to-end across the stack while leveraging AI to accelerate development velocity, improve code quality, and drive innovation. This role will have a Hybrid work schedule, with the expectation of working in an office (Columbus, OH, Chicago, IL, Hartford, CT or Charlotte, NC) 3 days a week (Tuesday through Thursday).

Requirements

Experience: 10+ years of professional software engineering experience, with 5+ years in hands-on full stack delivery roles
Education: Bachelor's degree in Computer Science, Software Engineering, or related field (or equivalent experience)
Frontend: Expert-level proficiency building production applications with modern JavaScript/TypeScript frameworks (React, Angular, or Vue.js), HTML5, CSS3, responsive design, and accessibility standards
Backend: Deep hands-on experience building and operating services with Java/Spring Boot, Node.js/Express, Python/FastAPI/Django, or C#/.NET
Cloud & DevOps: Strong hands-on experience deploying and operating on cloud platforms (AWS, GCP, or Azure), containerization (Docker, Kubernetes), and CI/CD tooling (Jenkins, GitHub Actions, GitLab CI)
Data: Proficiency building and optimizing data layers with SQL and NoSQL databases (PostgreSQL, MongoDB, DynamoDB, Redis), data modeling, and query tuning
API Implementation: Proven track record building RESTful APIs, GraphQL services, gRPC endpoints, or event-driven integrations (Kafka, RabbitMQ)
AI Tools Proficiency: - Advanced daily usage of AI coding assistants (GitHub Copilot, Cursor, or equivalent) - Experience integrating LLM APIs (OpenAI, Anthropic, GCP Vertex Service) into production applications - Familiarity with prompt engineering, fine-tuning, embeddings, and vector databases - Track record of measurably improving team velocity through AI tool adoption
Testing: Strong testing practices including unit, integration, and end-to-end testing with modern frameworks
Architecture: Working knowledge of distributed systems, microservices, and event-driven patterns; ability to contribute to and implement architectural designs

Nice To Haves

Experience building AI/ML-powered product features (chatbots, intelligent search, recommendation engines, content generation)
Familiarity with AI orchestration frameworks (LangChain, Semantic Kernel, AutoGen, CrewAI)
Experience with agent-based AI architectures and autonomous workflows
Contributions to open-source projects or published technical content
Experience in regulated industries (insurance, finance, healthcare) with security and compliance requirements
Certifications in cloud platforms (AWS Solutions Architect, GCP Professional Cloud Architect, etc.)

Responsibilities

Own features from concept through production release, writing and shipping high-quality code daily
Build, test, deploy, and iterate rapidly across frontend, backend, APIs, and data layers
Troubleshoot and resolve production issues, performance bottlenecks, and complex bugs
Implement and maintain CI/CD pipelines, automated testing, and deployment automation
Contribute to architectural decisions and design discussions as a practitioner, ensuring designs translate into solid implementations
Own system reliability, performance, and security for the services you build
Build and ship modern frontend applications using frameworks such as React, Angular, or Vue.js with TypeScript
Develop, deploy, and operate robust backend services using Java (Spring Boot), Node.js, Python, or .NET
Implement and optimize RESTful APIs, GraphQL endpoints, and event-driven integrations
Configure and manage cloud-native infrastructure (AWS or GCP) including containers, serverless, and infrastructure-as-code
Build and tune data layers across relational and NoSQL databases, caching, and message queues
Write clean, well-tested, production-ready code with a focus on maintainability and operational excellence
Leverage AI coding assistants (GitHub Copilot, Cursor, Claude Opus, etc.) to dramatically accelerate development workflows
Use AI tools for code generation, refactoring, test writing, documentation, and code review
Integrate large language model (LLM) APIs and AI/ML services into applications (e.g., OpenAI, GCP Vertex, AWS Bedrock)
Design and implement RAG (Retrieval-Augmented Generation) pipelines, prompt engineering strategies, and AI-powered features
Evaluate, select, and champion AI tools and practices that improve team productivity
Develop custom agents, workflows, and automations using AI platforms and frameworks (Vertex Studio, Semantic Kernel, CrewAI, etc.)
Establish guardrails, security practices, and governance for responsible AI usage in engineering
Influence engineering culture by evangelizing AI-augmented development practices
Train and upskill team members on effective use of AI tools in their daily workflows
Contribute to internal knowledge bases, tech talks, and communities of practice
Partner with product, design, and data science teams to identify AI-driven opportunities