Founding AI Engineer

About the Role: Founding AI Engineer

We’re building an early-stage startup (currently under the radar) that uses Generative AI + modern Machine Learning. We’re looking for a Founding AI Engineer to help shape the product and architecture from day one: design → build → iterate.

Responsibilities of a Founding AI Engineer

• Design and build GenAI/ML components end-to-end (POCs → production).
• Develop retrieval systems (RAG), embeddings pipelines, vector search, and evaluation tooling.
• Build data modelling + entity resolution for financial objects (entities, transactions, relationships, hierarchies).
• Implement large-scale data processing (batch + streaming where needed), and ensure reliability.
• Create agent/workflow logic that can reason over structured + unstructured data safely.
• Own model quality: metrics, offline evals, regression tests, prompt/versioning, guardrails.
• Partner closely with the founder(s) on product shaping, customer discovery, and prioritization.

Must-have Requirements

• Strong hands-on experience with GenAI / ML (LLMs, embeddings, RAG, tool-use, evaluation).
• Solid software engineering skills (you can ship production code, not just notebooks).
• Experience designing data flows and services (APIs, pipelines, storage, observability).
• Builder mindset: iterate fast, take ownership, make pragmatic tradeoffs.
• Comfortable in an early-stage environment with ambiguity and high responsibility.

Strong Advantages (Nice-to-have)

• Experience with data modeling, entity resolution, knowledge graphs, or graph databases (Neo4j / RDF / etc.).
• Experience with large-scale data processing (Spark, Beam, Flink, Kafka, dbt, Airflow).
• Familiarity with vector databases (pgvector, Pinecone, Weaviate, Milvus) and retrieval tuning.
• Experience with MLOps / LLMOps: deployment, monitoring, model/prompt versioning, quality gates.
• Security/enterprise readiness mindset (RBAC, audit logs, privacy-by-design).

Tech Stack

• Python (FastAPI), TypeScript as needed
• Postgres (+ pgvector) and/or a vector DB
• Graph layer (optional, depending on direction)
• Modern LLM tooling (LangChain/LlamaIndex or lightweight custom), eval harnesses
• Cloud (AWS/GCP), Docker/K8s (optional early, likely later)

Compensation

Equity-based initially (founding-level impact). Goal: raise funding and transition to salary + equity once funded (timeline depends on traction). You’ll be a core part of the company’s technical foundation.

Who this is Perfect For

• You want to be early, build real systems, and influence architecture + product decisions.
• You like hard problems in data + ML, and you care about shipping.
• You want to create something meaningful from scratch, not “join a mature org and optimize.”

Key skills/competency

• Generative AI
• Machine Learning
• Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)
• Data Modeling
• Entity Resolution
• Vector Databases
• MLOps/LLMOps
• Software Engineering
• Cloud Platforms (AWS/GCP)