Forward-Deployed AI Data Engineer

Flipside Crypto•Boston, MA

1d•Onsite

About The Position

This role is for someone who thrives in complex, messy enterprise data environments, not a clean, greenfield project. You will embed inside client environments to make AI agents work against data that was never prepared for them. You will solve specific problems for specific organizations using their actual data sources, such as CRMs, warehouses, email archives, and document repositories. Every engagement will result in measurable outcomes like leads written to CRM, pipelines running in production, or briefings delivered to decision-makers. You will work closely with the CTO and Enterprise Data Strategist on each account, acting as the person who makes the AI promise a reality.

Requirements

4–8 years combining hands-on data engineering with direct deployment or customer exposure (e.g., forward-deployed engineering, solutions engineering, data consulting, or technical implementation at a data or AI company).
Experience working inside enterprise data environments, understanding the reality of CRMs, warehouses, and legacy pipelines.
SQL fluency; proficiency in Python preferred.
Comfortable reading and writing API integrations.
Hands-on experience building or deploying AI agent workflows; understanding where LLMs break against real data problems.
Unstructured data instincts: ability to work with data that lacks schema, labels, or consistent format.
Bias toward output: prioritizing the accuracy and reliability of agent results over code elegance.
Client-facing comfort: ability to communicate effectively with technical stakeholders like CTOs.
Strong opinions on why AI deployments often fail due to data issues rather than model limitations.

Nice To Haves

Experience at a company running a forward-deployed or consultative technical model (e.g., Palantir, Scale AI).
Familiarity with blockchain data, DeFi, or institutional crypto infrastructure.
Experience with financial services or insurance data environments.

Responsibilities

Lead technical onboarding and implementation from data environment discovery through production deployment.
Build, configure, and troubleshoot data connectors, pipelines, and AI agent workflows inside client environments.
Work directly with Forge, Lattice, and Stratum — our agent framework, orchestration layer, and semantic intelligence system.
Serve as the primary technical point of contact for your accounts post-deployment.
Surface learnings from the field (product gaps, failure modes, recurring patterns) back to engineering.
Develop implementation playbooks from each engagement to improve future deployments.
Partner with the Enterprise Data Strategist and CEO on pre-sale scoping, technical discovery, and proof-of-concept builds.