Agileengine
Artificial Intelligence Engineer (Cal)
Agileengine, California, Missouri, United States, 65018
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. #J-18808-Ljbffr
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. #J-18808-Ljbffr