Artificial Intelligence Engineer Id42044

Job DescriptionAgileEngine is an Inc. **** 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 youABOUT THE ROLEAs 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.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.