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# Full Stack AI Engineer

- **Company:** The Aes Group, Inc
- **Location:** Boston, MA, United States
- **Contract:** Permanent contract
- **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, User Authentication, Microsoft Azure, C Sharp (Programming Language), Cloud Computing, Databases, Continuous Integration, Extract Transform Load (ETL), Python (Programming Language), PostgreSQL, Microsoft Software, OAuth, OpenID, Software Engineering, SQL Databases, TypeScript, Google Cloud, ReactJS, Retrieval-Augmented Generation, Large Language Models, Claude Code, Software Troubleshooting, Backend, Agentic-AI, Event Driven Architecture, Pgvector, Kubernetes, Front End Software Development, Api Management, Serverless Computing, Docker
- **Published:** November 2, 2026
- **Apply:** https://www.careerjet.com/jobad/us3f115527687d5ae4e428375d920f921f

## About the Role

We're placing much more value on practical engineering ability and technical judgment than on years of experience with specific languages or frameworks. Candidates with strong software engineering backgrounds and practical AI experience are just as relevant as those coming from AI application development.
The main capabilities we're looking for are:

 * Full-stack engineering: Experience developing production software, including APIs, frontend applications, databases, testing, and troubleshooting. Python is our primary backend language, but strong backend experience in another language is fine, with practical Python proficiency. For frontend development, experience with a modern TypeScript framework is sufficient; React is preferred.
 * AI-assisted development: Hands-on experience using agentic coding tools such as Claude Code or Codex to build, test, and troubleshoot software. They should understand how to guide these tools effectively, review generated code, and verify that implementations meet engineering standards.
 * AI application fundamentals: Experience building meaningful AI capabilities, including internal applications or substantial prototypes. Familiarity with LLM integration, prompting, structured outputs, tool calling, RAG fundamentals, and common reliability and security concerns.
 * AI evaluation and observability: This is particularly important. Candidates should understand how to determine whether an AI capability is actually functioning correctly, including offline evaluation, asynchronous evaluation of deployed interactions, runtime validation, and the use of telemetry to investigate failures and measure quality. We're looking for sound foundational understanding, not necessarily experience building sophisticated evaluation frameworks.
 * Cloud and production engineering: Working knowledge of Azure and/or GCP, Docker, CI/CD, SQL, API integrations, and authentication concepts such as OAuth 2.0/OIDC. They should be comfortable investigating issues in running applications and working within established engineering standards.

Experience with event-driven systems, ETL/CDC, PostgreSQL/pgvector, serverless architectures, Kubernetes, Vertex AI, Microsoft Foundry, or additional languages such as C# and Go would be beneficial, but none is individually required.