{"schemaVersion":"jobsearcher.job.v1","id":"2cc0c54f844e46ce69e85394","url":"https://jobsearcher.com/jobs/2cc0c54f844e46ce69e85394","canonicalUrl":"https://jobsearcher.com/jobs/2cc0c54f844e46ce69e85394","title":"Forward Deployed AI Engineer","description":"Forward Deployed AI Engineer\nBuild end-to-end products on a solid data foundation, with AI as a force multiplier.\nData Practice | Remote (Global) | Senior\nAbout TechTorch\nAt TechTorch, we’re building the future of intelligent work. Our mission is to help companies design, build, and deploy AI agents that automate complex, real-world workflows — delivering reliability, measurable ROI, and massive efficiency gains.\nHere, you won’t just be playing with prompts or running endless proofs of concept. You’ll ship production-grade AI systems that solve real problems across industries.\nYou’ll join a hands-on, fast-moving, ownership-driven team that thrives on building quickly, iterating fast, and seeing results in days — not months.\nAbout the Practice\nTechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full-stack AI applications across industries.\nWe work the way the best client-delivery teams now operate: small teams, deep ownership, no hand-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place.\nThe Role\nWe're looking for an engineer who builds across the full stack and owns the data underneath it. You can sit in a client session, shape the architecture, design the data foundation, and ship the application that runs on top of it — without handing off at the boundaries.\nThe work spans client delivery and internal accelerator development. You map the problem, structure the solution, and own the outcome from end to end. AI coding agents are central to how we build — not a novelty, but the daily layer that lets a small team cover a lot of ground.\nWhat You'll Do\nOwn work end to end — from discovery and solution shaping through system design, build, and production deployment.\nDesign and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production.\nBuild full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable.\nUse AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality.\nDesign 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 Plugins.\nOrchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job.\nStand up and own CI/CD and cloud deployments on AWS and Azure.\nTranslate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences.\nContribute reusable accelerators and technical assets back to the Data Practice.\nMust Have\nWe're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either.\nData Engineering Foundation\nData modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend.\nHands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule).\nSlowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each applies.\ndbt Experience— modular SQL transformations, tests, documentation, and incremental strategies.\nAdvanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse).\nData quality thinking — testing, validation, and lineage treated as first-class, not afterthoughts.\nFull-Stack AI Product Development\nPython as a primary language — services, automation, and data work alike.\nFastAPI — async REST API design, dependency injection, testing.\nA modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibility.\nPostgreSQL — schema design, query optimization, indexing.\nSystem design — can architect from a blank page: services, boundaries, trade-offs, and scale.\nAI-paired engineering — uses an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, and can speak concretely to how.\nCI/CD and cloud deployment ownership on AWS or Azure, without heavy support.\nWays of Working\nComfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineering.\nCustomer-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 conversation.\nEnd-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff.\nNice to Have\nNot required to apply — but these are the things that make a candidate stand out.\nStandout 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 inconsistencies.\nAgentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.\nRAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation.\nExperience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.\nWorkflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).\nStreaming data patterns — Kafka, Spark Streaming, or Flink.\nVector databases — Pinecone, Weaviate, Qdrant, or pgvector.\nExperiment tracking — MLflow, Weights & Biases, or similar.\nContributions to open-source AI or data tooling, or to internal accelerators and frameworks.\nMulti-cloud or hybrid cloud architecture exposure.\nYou Might Be a Fit If...\nYou're comfortable designing a data model in the morning and shipping a FastAPI + Next.js feature on top of it in the afternoon.\nYou treat an AI coding agent as a force multiplier — you've genuinely changed how you build, not just turned on autocomplete.\nYou can explain an SCD strategy to an engineer and a data-quality risk to a business stakeholder in the same conversation.\nYou've shipped real things in production — not just demos or PoCs.\nYou're opinionated about system and data design, and can back it up.\nWhat We Offer\nFully remote — work from anywhere, globally.\nSemi-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.\nHigh-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.\nA team that takes AI tooling seriously and expects you to use it, not just name-drop it.\nAccess to the full modern data and AI stack — no one-tool shops.\nRoom to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.","company":"Techtorch","rawCompany":"techtorch","city":"Denver","state":"CO","isRemote":false,"isActive":false,"createdAt":"2026-08-15T13:04:13.756Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"},{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"}],"industries":[{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"},{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Forward Deployed AI Engineer","description":"Forward Deployed AI Engineer\nBuild end-to-end products on a solid data foundation, with AI as a force multiplier.\nData Practice | Remote (Global) | Senior\nAbout TechTorch\nAt TechTorch, we’re building the future of intelligent work. Our mission is to help companies design, build, and deploy AI agents that automate complex, real-world workflows — delivering reliability, measurable ROI, and massive efficiency gains.\nHere, you won’t just be playing with prompts or running endless proofs of concept. You’ll ship production-grade AI systems that solve real problems across industries.\nYou’ll join a hands-on, fast-moving, ownership-driven team that thrives on building quickly, iterating fast, and seeing results in days — not months.\nAbout the Practice\nTechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full-stack AI applications across industries.\nWe work the way the best client-delivery teams now operate: small teams, deep ownership, no hand-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place.\nThe Role\nWe're looking for an engineer who builds across the full stack and owns the data underneath it. You can sit in a client session, shape the architecture, design the data foundation, and ship the application that runs on top of it — without handing off at the boundaries.\nThe work spans client delivery and internal accelerator development. You map the problem, structure the solution, and own the outcome from end to end. AI coding agents are central to how we build — not a novelty, but the daily layer that lets a small team cover a lot of ground.\nWhat You'll Do\nOwn work end to end — from discovery and solution shaping through system design, build, and production deployment.\nDesign and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production.\nBuild full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable.\nUse AI coding agents (Claude Code or equivalent) as a primary build accelerator to move from spec to working software quickly, without sacrificing judgment or quality.\nDesign 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 Plugins.\nOrchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job.\nStand up and own CI/CD and cloud deployments on AWS and Azure.\nTranslate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences.\nContribute reusable accelerators and technical assets back to the Data Practice.\nMust Have\nWe're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either.\nData Engineering Foundation\nData modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend.\nHands-on data pipeline experience — ETL/ELT design across batch and incremental loads, built and maintained in production (not just SQL scripts on a schedule).\nSlowly Changing Dimensions (SCD) and change-data handling — knows the patterns and when each applies.\ndbt Experience— modular SQL transformations, tests, documentation, and incremental strategies.\nAdvanced SQL and at least one modern data platform in depth (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse).\nData quality thinking — testing, validation, and lineage treated as first-class, not afterthoughts.\nFull-Stack AI Product Development\nPython as a primary language — services, automation, and data work alike.\nFastAPI — async REST API design, dependency injection, testing.\nA modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibility.\nPostgreSQL — schema design, query optimization, indexing.\nSystem design — can architect from a blank page: services, boundaries, trade-offs, and scale.\nAI-paired engineering — uses an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, and can speak concretely to how.\nCI/CD and cloud deployment ownership on AWS or Azure, without heavy support.\nWays of Working\nComfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineering.\nCustomer-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 conversation.\nEnd-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff.\nNice to Have\nNot required to apply — but these are the things that make a candidate stand out.\nStandout 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 inconsistencies.\nAgentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.\nRAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation.\nExperience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.\nWorkflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows).\nStreaming data patterns — Kafka, Spark Streaming, or Flink.\nVector databases — Pinecone, Weaviate, Qdrant, or pgvector.\nExperiment tracking — MLflow, Weights & Biases, or similar.\nContributions to open-source AI or data tooling, or to internal accelerators and frameworks.\nMulti-cloud or hybrid cloud architecture exposure.\nYou Might Be a Fit If...\nYou're comfortable designing a data model in the morning and shipping a FastAPI + Next.js feature on top of it in the afternoon.\nYou treat an AI coding agent as a force multiplier — you've genuinely changed how you build, not just turned on autocomplete.\nYou can explain an SCD strategy to an engineer and a data-quality risk to a business stakeholder in the same conversation.\nYou've shipped real things in production — not just demos or PoCs.\nYou're opinionated about system and data design, and can back it up.\nWhat We Offer\nFully remote — work from anywhere, globally.\nSemi-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.\nHigh-autonomy, high-ownership work across the full arc of real client problems — not toy datasets or boxed-in tickets.\nA team that takes AI tooling seriously and expects you to use it, not just name-drop it.\nAccess to the full modern data and AI stack — no one-tool shops.\nRoom to grow toward data architecture, platform leadership, or AI engineering depth, depending on where you want to take it.","datePosted":"2026-08-15T13:04:13.756Z","dateModified":"2026-08-15T13:04:13.756Z","hiringOrganization":{"@type":"Organization","name":"Techtorch","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Denver","addressRegion":"CO","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"2cc0c54f844e46ce69e85394"},"url":"https://jobsearcher.com/jobs/2cc0c54f844e46ce69e85394"}}