[Remote] Full Stack AI Engineer (Data)

Note: The job is a remote job and is open to candidates in USA. TechTorch is building the future of intelligent work, focusing on designing, building, and deploying AI agents for complex workflows. They are seeking a Full Stack AI Engineer who will own the end-to-end development of AI-driven applications, from data foundation design to production deployment, while leveraging AI coding agents for efficiency.ResponsibilitiesOwn work end to end — from discovery and solution shaping through system design, build, and production deploymentDesign and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in productionBuild full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usableUse AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or qualityDesign and build AI capabilities where they fit — RAG pipelines, agentic workflows, and LLM-in-the-loop processing — and compose them via MCP servers, Skills, and PluginsOrchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the jobStand up and own CI/CD and cloud deployments on AWS and AzureTranslate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiencesContribute reusable accelerators and technical assets back to the Data PracticeSkillsWe're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with eitherData modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defendHands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule)Slowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each appliesDbt Experience— modular SQL transformations, tests, documentation, and incremental strategiesAdvanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse)Data quality thinking — testing, validation, and lineage treated as first-class, not afterthoughtsPython as a primary language — services, automation, and data work alikeFastAPI — async REST API design, dependency injection, testingA modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibilityPostgreSQL — schema design, query optimization, indexingSystem design — can architect from a blank page: services, boundaries, trade-offs, and scaleAI-paired engineering — uses an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, and can speak concretely to howCI/CD and cloud deployment ownership on AWS or Azure, without heavy supportComfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineeringCustomer-first mindset — anchors decisions in what the stakeholder is actually trying to accomplish, and can move fluidly between the engineer's view and the business owner's in the same conversationEnd-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoffStandout differentiator — Commercial data fluency: Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistenciesAgentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state managementRAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluationExperience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering roleWorkflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows)Streaming data patterns — Kafka, Spark Streaming, or FlinkVector databases — Pinecone, Weaviate, Qdrant, or pgvectorExperiment tracking — MLflow, Weights & Biases, or similarContributions to open-source AI or data tooling, or to internal accelerators and frameworksMulti-cloud or hybrid cloud architecture exposureBenefitsFully remote — work from anywhere, globally.Semi-annual team offsites — we come together in person at least twice a year to connect, recharge, and do the work that's better face-to-face.High-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.A team that takes AI tooling seriously and expects you to use it, not just name-drop it.Access to the full modern data and AI stack — no one-tool shops.Room to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.Company OverviewTechTorch is a AI powered Tech Consulting company It was founded in 2021, and is headquartered in San Mateo, California, USA, with a workforce of 51-200 employees. Its website is https://www.techtorch.io/.Company H1B SponsorshipTechTorch has a track record of offering H1B sponsorships, with 4 in 2025. Please note that this does not guarantee sponsorship for this specific role.