Full Stack AI + Data Engineer
About TechTorch TechTorch is a high-growth enterprise technology consultancy that partners with the world’s leading private equity-backed businesses. We deliver AI-powered solutions, accelerators, and data-driven transformation initiatives that drive measurable value at speed and scale. Our mission is to redefine enterprise technology consulting for private equity. We combine the agility of a scale-up with the discipline and rigor demanded by the most sophisticated investors and operators. TechTorch was founded by seasoned leaders — including former Bain consultants, CIOs, and tech executives — with deep expertise in technology, transformation, and value creation. We were built to deliver results that matter. About the Team TechTorch's Data Practice sits at the intersection of enterprise data and applied AI. We design and build AI-native systems that don't just analyze the past — they actively drive decisions. Our work spans data infrastructure and pipelines, intelligent automation, and full-stack AI applications across industries. We're a team of engineers and architects who take things from a whiteboard to production. We don't hand off at boundaries — we own outcomes.
About the Role
We're looking for a Full Stack AI + Data Engineer who owns the complete product lifecycle — from discovery and ideation through to production deployment. Not a specialist who stops at the API boundary. Someone who can sit in a client session, shape the solution architecture, build the stack, and ship it. The role sits at the center of client delivery and internal accelerator development — someone who can own the full arc of a project and be the person who makes things real.
What You'll Do
Own the full product lifecycle — from discovery and ideation through system design, build, and production deployment Design and build RAG pipelines, agentic workflows, and multi-agent systems using LangGraph and the broader AI agent ecosystem Build and compose AI capabilities using MCP servers, Skills, and Plugins — and stay sharp on how these primitives are evolving Develop Next.js frontends that make complex AI workflows feel intuitive to end users Build Python-based APIs and backend services using FastAPI, with PostgreSQL as the primary data store Design and implement automation workflows using Celery, Temporal, or equivalent orchestration tools Architect and maintain data pipelines (ETL/ELT), data models, and dbt-based analytics engineering layers Set up and own CI/CD pipelines and cloud deployments on AWS and Azure Leverage AI-paired programming tools (Claude Code or similar) as a daily accelerator — not as a crutch, but as a force multiplier Translate ambiguous client requirements into clear system designs, and communicate trade-offs across both technical and business audiences Contribute to reusable internal accelerators and technical assets within the Data Practice What You Bring Production-grade experience across AI engineering, full-stack development, and data — with genuine depth, not just surface familiarity. AI & GenAI Engineering RAG pipeline design — retrieval strategies, vector stores, chunking, re-ranking, and evaluation Agentic AI systems using LangGraph — multi-agent coordination, tool use, memory, and state management Building and composing AI capabilities via MCP servers, Skills, and Plugins AI-paired accelerated programming — proficient at using Claude Code or a comparable agentic coding tool as a daily productivity layer Full Stack Development Python — primary language, used for services, automation, and data work FastAPI — async REST API design, dependency injection, testing Next.js — component architecture, server-side rendering, state management, and UX sensibility PostgreSQL — schema design, query optimization, indexing System Design — can architect a system from a blank page: services, boundaries, trade-offs, and scale Automation & Workflow Orchestration Building automation workflows using Celery or Temporal — task queuing, retries, distributed scheduling Event-driven patterns and async processing at the application layer Data Engineering ETL/ELT pipeline design — batch, incremental, and event-driven ingestion patterns Data pipelining and modeling — dimensional modeling, EDW design, schema governance dbt — transformation logic, testing, documentation, and analytics engineering best practices DevOps & Cloud CI/CD and deployment — GitHub Actions or equivalent, containerized delivery, environment management Exposure to common cloud services on AWS and Azure — compute, storage, managed databases, serverless You might be a fit if... You've shipped full-stack AI applications in production — not just demos or PoCs You're comfortable switching between designing a data model in the morning and debugging an agentic pipeline in the afternoon You get restless when handing things off — you'd rather own it end to end You can explain a RAG pipeline to a business stakeholder and architecture trade-offs to an engineering team in the same day You're opinionated about system design and can back it up Nice to have Experience in a consulting or client-delivery environment Contributions to open-source AI or data tooling Exposure to multi-cloud or hybrid cloud architectures Knowledge of MLflow, Weights & Biases, or similar experiment tracking tools Familiarity with streaming data patterns (Kafka, Spark Streaming) What we Offer Fully remote position — work from anywhere, globally High-autonomy, high-ownership work — you drive the technical direction Exposure to cutting-edge AI engineering across multiple industries and use cases Opportunity to shape the Practice's internal accelerators and reusable assets A team of sharp engineers who take craft seriously and skip the bureaucracy