Job DescriptionAgileEngine 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
- Experience level: 4+ years as a software engineer and at least 2+ years at an AI-first company or building AI-powered applications.
- Proficiency in Python and TypeScript, with experience shipping production code.
- Hands-on experience deploying AI/LLM workflows into production (LangChain, LlamaIndex, vector DBs, orchestration frameworks, etc.).
- Familiarity with GCP.
- Experience building and maintaining APIs, data pipelines, or full-stack applications.
- Startup DNA: thrives in ambiguity, biased toward action, problem-first mindset, and high ownership.
- English: Upper-Intermediate English level.
NICE TO HAVES
- Familiarity with deploying production-grade systems.
- Experience with parsing unstructured data, optimization algorithms, or time-series forecasting.
- Background in energy, utilities, or IoT data (not required, but valuable context).
- Prior experience in a founding or early-stage engineering role.
PERKS AND BENEFITS
- Professional growth: Accelerate your professional journey with mentorship, TechTalks, and personalized growth roadmaps.
- Competitive compensation: We match your ever-growing skills, talent, and contributions with competitive USD-based compensation and budgets for education, fitness, and team activities.
- A selection of exciting projects: Join projects with modern solutions development and top-tier clients that include Fortune 500 enterprises and leading product brands.
- Flextime: Tailor your schedule for an optimal work-life balance, by having the options of working from home and going to the office – whatever makes you the happiest and most productive.
RequirementsExperience 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.