Artificial Intelligence Engineer ID42044

Job Description

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI / ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

As a Senior AI Engineer, you’ll build AI-powered systems that turn complex data into actionable insights, tackling high-impact challenges with modern cloud and LLM workflows. You’ll shape technical direction, influence team culture, and apply AI-first thinking to real-world problems, driving innovation and measurable business value in a fast-paced, collaborative environment.

WHAT YOU WILL DO

• Build AI Applications : Design and deploy intelligent systems that parse tariffs, optimize utility spend, and automate workflows.
• Productionize Agent Workflows : Integrate cutting-edge AI models into robust pipelines that run reliably in real-world environments.
• Full-Stack Development : Build APIs, backend services, and frontend integrations using Python and TypeScript as needed.
• Leverage Cloud at Scale : Deploy and maintain systems on GCP (or AWS), ensuring scalability, reliability, and performance.
• Iterate Rapidly : Prototype, test, and launch features quickly while maintaining production quality.
• Shape Foundations : Establish engineering standards, architecture principles, and AI-first practices for the company.

MUST HAVES

NICE TO HAVES

PERKS AND BENEFITS

Requirements

Experience level : 4+ years as a software engineer and at least 2+ years at an AI-first company or building AI-powered applications. Production engineering : Professional experience building and maintaining APIs, data pipelines, or full-stack applications in Python and TypeScript. LLM workflow deployment : Hands-on deploying AI / LLM workflows to production (e.g., LangChain, LlamaIndex, orchestration frameworks, vector databases). Startup DNA : Thrives in ambiguity, bias to action, problem-first mindset, and high ownership. RAG in production : Proven track record shipping document-centric RAG (retrieval, chunking, embeddings / vector DBs, re-ranking) with OpenAI, structured tool / JSON outputs, and streaming responses. RAG evaluation : Hands-on use of RAGAS and / or TruLens (faithfulness, answer relevance, context precision / recall) with measurable quality gates. Guardrails & safety : JSON Schema / Pydantic validation, moderation and grounding checks, plus red-teaming practices in production. Cloud production (GCP-first) : Experience operating services on Cloud Run / GKE, using BigQuery (consumed in Looker) and Firestore for app state / permissions; strong CI / CD discipline. (AWS familiarity is a plus / transferable.) Scraping / ingestion at scale : Built and operated pipelines with authentication (e.g., multi-tenant logins), robust parsing / storage, and audit-ready artifacts (data lineage, repeatability). Observability & controls : Structured logging, tracing (e.g., OpenTelemetry), metrics; cost / latency guardrails and safe releases (feature flags, canary, rollback) meeting p95 / p99 SLOs. English : Upper-Intermediate English level.