---
title: 'Forward Deployed AI Engineer at Techtorch'
canonical: 'https://feeny.ai/job/forward-deployed-ai-engineer-techtorch-eu-hkdq1k6ejn2b'
type: 'job'
last_seen: '2026-09-09'
---

# Forward Deployed AI Engineer at Techtorch

- **Company:** Techtorch
- **Location:** EU +, United Kingdom
- **Employment:** full-time
- **Posted:** 2026-08-10
- **Last confirmed live:** 2026-09-09
- **Apply:** https://jobs.ashbyhq.com/techtorch/878eaa7b-6ec8-42f4-974e-fde68db3582d

## Job description

Forward Deployed AI Engineer

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

Data Practice | Remote (Poland) | Senior

## About TechTorch

At TechTorch, we’re building the future of intelligent work. Our mission is to help companies design, build, and deploy AI agents that automate complex, real-world workflows — delivering reliability, measurable ROI, and massive efficiency gains.

Here, you won’t just be playing with prompts or running endless proofs of concept. You’ll ship production-grade AI systems that solve real problems across industries.

You’ll join a hands-on, fast-moving, ownership-driven team that thrives on building quickly, iterating fast, and seeing results in days — not months.

## About the Practice

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 work the way the best client-delivery teams now operate: small teams, deep ownership, no hand-offs at boundaries. We take problems from a client whiteboard to production, and we let AI do the heavy lifting wherever it earns its place.

## The Role

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.

Must Have

We're looking for genuine production depth across data engineering and full-stack development — not surface familiarity with either.

Data Engineering Foundation

- 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 (not just SQL scripts on a schedule).
- 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.

Full-Stack AI Product Development

- 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, and can speak concretely to how.
- CI/CD and cloud deployment ownership on AWS or Azure, without heavy support.

Ways of Working

- 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, and can move fluidly between the engineer's view and the business owner's in the same conversation.
- End-to-end ownership instinct — takes a problem from discovery to production and owns the outcome, rather than passing it along at each handoff.

## Nice to Have

Not required to apply — but these are the things that make a candidate stand out.

Standout differentiator — Commercial data fluency: Experience evaluating how commercial data flows across CRM (ideally Salesforce) and ERP (ideally NetSuite) from opportunity to order to invoice, with the ability to diagnose, document, and resolve inconsistencies.

- 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.

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.
- You treat an AI coding agent as a force multiplier — you've genuinely changed how you build, not just turned on autocomplete.
- You can explain an SCD strategy to an engineer and a data-quality risk to a business stakeholder in the same conversation.
- You've shipped real things in production — not just demos or PoCs.
- You're opinionated about system and data design, and can back it up.

## 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 — not toy datasets or boxed-in tickets.
- A team that takes AI tooling seriously and expects you to use it, not just name-drop 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, depending on where you want to take it.

## About Techtorch

## Company Overview
- **One-liner**: TechTorch is an AI-native operational execution partner that deploys production-ready agentic solutions to automate revenue workflows and drive EBITDA impact for Private Equity-backed companies.
- **Entity Type**: Private (Venture-backed)
- **Headquarters**: Woodside, California, United States
- **Founded**: 2021
- **Founders**: Miguel Vasconcelos (Co-Founder & Chief Product Officer) and Jordi (last name not publicly available)

## Core Business
- **Primary industry**: AI-Powered Enterprise Technology (ET) Solutions and IT Consulting
- **Target customers**: Private Equity-backed portfolio companies (B2B, Enterprise)
- **Mission statement**: To operationalize winning strategies in commercial excellence, enterprise data, and AI use cases to drive successful outcomes at unparalleled speed.

## Products & Services
- **Beacon Framework Library**: A production-tested library of ~80% ready operational accelerators for deploying AI-native revenue workflows across marketing, sales, deal desk, customer operations, and commercial data management.
- **Beacon Revenue Manager**: An agentic solution that uses AI agents to automatically detect invoice errors, pricing inconsistencies, missed discounts, and unbilled usage — reducing revenue leakage to 0%.
- **Beacon Quote Manager**: An agentic solution that generates accurate quotes instantly, delivering 90% faster quote creation with zero pricing errors.
- **Marketing Operations**: Agentic automation for campaign execution, lead routing, enrichment, and funnel operations.
- **Sales Operations**: Agentic automation for CRM workflows, forecasting, and seller operations (targeting 40%+ seller productivity).
- **Deal Desk**: Agentic acceleration of pricing, approvals, and quote-to-cash workflows.
- **Customer Operations**: Agentic automation for onboarding, renewals, and customer workflows (targeting earlier churn identification).

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Venture Round raised in December 2022 from 1 investor (amount and lead investor not publicly disclosed)
- **Notable Investors/Partners**: The company has 150+ Private Equity-owned clients worldwide and operates in 14 countries. The company acquired AstuteForce on May 20, 2024.
- **Growth Signals**: Headcount grew 71.2% year-over-year (81 employees as of latest data). LinkedIn followers grew 162.9% year-over-year. The company operates in 14 countries including the US, Poland, Germany, UK, and Spain. They have a documented cadence of 4–8 week deployments.

## Competitive Advantages
- **Speed of Deployment**: Production-ready operational workflows deployed in 4–8 weeks, not quarters — a core differentiator from traditional consulting.
- **Operator DNA**: Team of 150+ former operators, consultants, and AI builders from Bain, McKinsey, Accenture, Meta, LinkedIn, Cisco, Nike, and leading AI startups.
- **EBITDA-Focused Outcomes**: Every deployment is tied directly to operational KPIs, revenue acceleration, and EBITDA improvement.
- **Reusable Playbooks**: Portfolio-scale execution with reusable playbooks and operational architectures designed for rapid deployment across portfolio companies.
- **~80% Ready Frameworks**: Pre-built accelerators that reduce deployment time significantly.

## Strategic Focus
- **AI-Native Revenue Operations**: Deep focus on automating the entire revenue engine (marketing, sales, deal desk, customer operations, commercial data) with agentic AI.
- **PE-Backed Company Growth**: Serving the specific needs of Private Equity portfolio companies, helping them achieve rapid operational improvements and EBITDA gains.
- **Rapid Deployment Model**: Continuously refining their 4–8 week deployment methodology to outpace traditional consulting models.
- **Talent Acquisition**: Aggressively hiring top AI and data engineering talent to scale their delivery capacity.

## Why Work Here
- **Culture & Values**: Described as a "bunch of non-conformist dreamers" who reject the traditional consulting model of "bloated teams, endless meetings, bureaucracy, excuses, and politics." The company values outcomes over time and resources, and emphasizes working hard and playing hard.
- **Work Environment**: Remote-first with hubs in Silicon Valley (Woodside, CA), Seattle, and Warsaw, Poland. The company also has offices in Kraków, Poland.
- **Pace & Impact**: Employees work on forward-deployed, PE-portfolio engagements that ship production AI in 4–8 weeks with end-to-end ownership. The pace is described as "production-first" with tight sprints and rapid decision-making.
- **Team Composition**: A mix of former top-tier consultants (Bain, McKinsey, Accenture) and Big Tech engineers (Meta, LinkedIn, Cisco, Nike), creating a blend of strategic and technical expertise.
- **Recent Job Postings**: Active roles include Full Stack AI + Data Engineer, Delivery Manager (CPQ / RevOps / Lead-to-Cash), AI Developer, AI Data Engineer, Data Architect, and Recruiter.
- **Key Insight for Candidates**: The consulting cadence brings tight deadlines, shifting scopes, and client-driven priorities — suitable for those who thrive on high autonomy and measurable impact.

## Sources
1. [techtorch.io](https://techtorch.io/)
2. [techtorch.io/about](https://techtorch.io/about)
3. [linkedin.com/company/techtorch](https://www.linkedin.com/company/techtorch)
4. [builtin.com/company/techtorch](https://builtin.com/company/techtorch)
5. [builtin.com/company/techtorch/faq/workplace-perception](https://builtin.com/company/techtorch/faq/workplace-perception)

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