TechTorch
Full Stack AI Engineer (Data)
United States
Sponsorship not specifiedDetected 35 days ago
PythonNext.jsFastAPIFull-Stack DevelopmentSQLPostgreSQLSnowflakeDatabricksVector DatabasesAWSAzureCI/CDTemporalAPI DevelopmentRESTKafkaSparkAirflowdbtData EngineeringLLMsRAGAgentic AILangGraph
About the role
- The work spans client delivery and internal accelerator development.
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.
- 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.
- 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).
Nice to have
- Agentic AI depth - LangGraph or comparable: multi-agent coordination, tool use, memory, and state management.
- Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role.
- 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.
Skills
- RAG engineering - retrieval strategies, vector stores, chunking, re-ranking, and evaluation.
- 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.
- Fully remote - work from anywhere, globally.
- High-autonomy, high-ownership work across the full arc of real client problems - not toy datasets or boxed-in tickets.
- Access to the full modern data and AI stack - no one-tool shops.
Compensation
- 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.
Company info
- We're looking for genuine production depth across data engineering and full-stack development - not surface familiarity with either.
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