Full Stack AI Engineer

Role Description

Build end-to-end products on a solid data foundation, with AI as a force multiplier.

We'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.

The 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.

What You'll Do

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.

Qualifications

Genuine production depth across data engineering and full-stack development.

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.

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.

CI/CD and cloud deployment ownership on AWS or Azure, without heavy support.

Requirements

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.

End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome.

Nice to Have

Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite).

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.

What We Offer

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.

A team that takes AI tooling seriously and expects you to use 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.