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—shipping production-grade features quickly while maintaining quality.
- Document-centric RAG with OpenAI : Implement RAG using structured tool / JSON outputs, streaming and batch flows, with robust guardrails, red-teaming, and RAG evaluation (e.g., RAGAS, TruLens).
- Productionize agent workflows : Integrate cutting-edge AI models into resilient pipelines and services that run reliably in real-world environments.
- Scraping / ingestion at scale : Create pipelines for automated utility logins → parse / store bills & usage → anomaly detection → “ready-to-audit” bills, with full auditability and data lineage.
- Production services on cloud : Build and operate on GCP (Cloud Run and / or GKE); use BigQuery as the analytics backbone feeding Looker; leverage Firestore for app state and permissions. (AWS experience transferable.)
- APIs & full-stack delivery : Develop APIs and backend services in Python / TypeScript and collaborate with frontend integrations as needed.
- Reliability, cost & latency controls : Lead feature-flagged rollouts, implement end-to-end tracing, and enforce p95 / p99 SLOs, budgets, and rate-limiting to balance performance and spend.
- Iterate rapidly : Prototype, test, and launch features fast; harden successful prototypes into scalable, observable, secure services.
- Shape foundations : Establish engineering standards, architecture principles, and AI-first practices that set the bar 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.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.NICE TO HAVES
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.Vector databases (pgvector, Pinecone, Weaviate) and re-ranking experience.GCP IaC (Terraform), Secrets / IAM hardening; Looker / LookML modeling.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.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.