The provider of solutions and services for the full cycle of software product development.

AgileEngine

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.