Techtorch - Full Stack AI Engineer (Data)
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Requirements
• We're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either. • Data Engineering Foundation • Data modeling and schema design — dimensional modeling, normalization trade-offs, and EDW/warehouse schema design you can defend. • Hands-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 applies. • dbt Experience— modular SQL transformations, tests, documentation, and incremental strategies. • Advanced 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 afterthoughts. • Full-Stack AI Product Development • Python as a primary language — services, automation, and data work alike. • FastAPI — async REST API design, dependency injection, testing. • A modern frontend, ideally Next.js — component architecture, SSR, state management, and real UX sensibility. • System design — can architect from a blank page: services, boundaries, trade-offs, and scale. • AI-paired engineering — uses an agentic coding tool (Claude Code, Cursor, or comparable) as a genuine daily workflow accelerator, and can speak concretely to how. • CI/CD and cloud deployment ownership on AWS or Azure, without heavy support. • Ways of Working • Ways of Working • Comfortable in client-facing delivery — can represent TechTorch technically and translate between business and engineering. • Customer-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. • End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff. • Not required to apply — but these are the things that make a candidate stand out. • Standout 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. • Standout differentiator — Commercial data fluency: • Agentic AI depth — LangGraph or comparable: multi-agent coordination, tool use, memory, and state management. • RAG engineering — retrieval strategies, vector stores, chunking, re-ranking, and evaluation. • Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role. • Workflow orchestration breadth across multiple tools (Airflow, Dagster, Prefect, Temporal, ADF, Databricks Workflows). • Streaming data patterns — Kafka, Spark Streaming, or Flink. • Vector databases — Pinecone, Weaviate, Qdrant, or pgvector. • Experiment tracking — MLflow, Weights & Biases, or similar. • Contributions to open-source AI or data tooling, or to internal accelerators and frameworks. • Multi-cloud or hybrid cloud architecture exposure. • You Might Be a Fit If... • You're comfortable designing a data model in the morning and shipping a FastAPI + Next.js feature on top of it in the afternoon. • You treat an AI coding agent as a force multiplier — you've genuinely changed how you build, not just turned on autocomplete. • You can explain an SCD strategy to an engineer and a data-quality risk to a business stakeholder in the same conversation. • You've shipped real things in production — not just demos or PoCs. • You're opinionated about system and data design, and can back it up.
Responsibilities
• Own work end to end — from discovery and solution shaping through system design, build, and production deployment. • Design and build the data foundation: data models, schema design, dimensional modeling, ETL/ELT pipelines, and slowly changing dimensions (SCD) that hold up in production. • Build full-stack applications on top of that foundation — Python/FastAPI services and Next.js frontends that make data and AI workflows usable. • Use 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. • Design 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. • Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal — choosing the right tool for the job. • Stand up and own CI/CD and cloud deployments on AWS and Azure. • Translate ambiguous client requirements into clear designs and communicate trade-offs to both technical and business audiences. • Contribute reusable accelerators and technical assets back to the Data Practice.
Benefits
• Fully 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.
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