Forward Deployed Engineer

The role

You will own work end to end from discovery and solution shaping through system design, build, and production deployment. You will design data foundations and build full-stack applications using Python, FastAPI, and Next.js.

What you will do

• Own work end to end from discovery to production deployment
• Design and build the data foundation including data models, schema design, and ETL/ELT pipelines
• Build full-stack applications using Python/FastAPI services and Next.js frontends
• Use AI coding agents as a primary build accelerator
• Design and build AI capabilities like RAG pipelines and agentic workflows
• Orchestrate pipelines and automation with tools like Airflow, Dagster/Prefect, Celery, or Temporal
• Stand up and own CI/CD and cloud deployments on AWS and Azure
• Translate ambiguous client requirements into clear designs
• Contribute reusable accelerators and technical assets back to the Data Practice

Required qualifications

• Minimum 5 years of hands-on professional experience in data engineering, full-stack AI Product development, or related software engineering roles
• Data modeling and schema design
• Hands-on data pipeline experience with ETL/ELT design
• Knowledge of Slowly Changing Dimensions (SCD)
• dbt Experience
• Advanced SQL
• Experience with at least one modern data platform (e.g., Snowflake, Databricks, or a comparable cloud warehouse/lakehouse)
• Data quality thinking including testing, validation, and lineage
• Python as a primary language
• FastAPI
• Modern frontend experience, ideally Next.js
• PostgreSQL

Nice to have

• Commercial data fluency: Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite)
• Agentic AI depth - LangGraph or comparable
• RAG engineering
• Experience in a consulting or client-delivery environment, or a forward-deployed / embedded engineering role
• Workflow orchestration breadth across multiple tools
• 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

Benefits

• Semi-annual team offsites
• High-autonomy, high-ownership work
• A team that takes AI tooling seriously
• Access to the full modern data and AI stack
• Room to grow toward data architecture, platform leadership, or AI engineering depth

About the company

TechTorch is building the future of intelligent work by helping companies design, build, and deploy AI agents that automate complex, real-world workflows. Their Data Practice sits at the intersection of enterprise data and applied AI.