> Markdown version of [/jobs/ext/207088-full-stack-ai-data-engineer](https://www.wearedevelopers.com/jobs/ext/207088-full-stack-ai-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Full Stack AI + Data Engineer - **Company:** TECHTORCH, INC - **Location:** United States (Remote available) - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Application Layers, Microsoft Azure, Computer Programming, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), Data Stores, Data Warehousing, Software Debugging, Dependency Injection, DevOps, Dimensional Modeling, Github, Python (Programming Language), PostgreSQL, Open Source Technology, Pair Programming, Query Optimization, Cloud Services, Next.js, Software Deployment, Data Streaming, Workflow Management Systems, Multi-Agent Systems, Multi-Cloud, HybridCloud, Indexer, Backend, Fastapi, Build Management, Apache Kafka, Spark Streaming, Machine Learning Operations, Celery, Virtual Agents, Restful APIs, Automation Anywhere, Serverless Computing - **Published:** May 27, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=6740052e5af1c2fe ## About the Role Do you have experience in Stakeholder management?, 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, * 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) ## Description 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, * 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, * 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 ## Related Videos - [Celery on AWS ECS - the art of background tasks & continuous deployment](https://www.wearedevelopers.com/videos/561-celery-on-aws-ecs-the-art-of-background-tasks-continuous-deployment) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Innovating Developer Tools with AI: Insights from GitHub Next](https://www.wearedevelopers.com/videos/1268-innovating-developer-tools-with-ai-insights-from-github-next) - [The AI-Ready Stack: Rethinking the Engineering Org of the Future](https://www.wearedevelopers.com/videos/1706-the-ai-ready-stack-rethinking-the-engineering-org-of-the-future) - [Walking into the era of Supply Chain Risks](https://www.wearedevelopers.com/videos/376-walking-into-the-era-of-supply-chain-risks) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers)