Full Stack AI Engineer (Data)

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.

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

Requirements:

• genuine production depth across data engineering and full-stack development — not surface familiarity with either
• 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
• 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
• PostgreSQL — schema design, query optimization, indexing
• 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
• 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
• 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
• 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