Lead AI/ML Engineer - Remote, Equity on Conversion

loom ai; Northern (KY); Hybrid; USD 120,000 - 180,000; Full time

Benefits offered by this job

Equity opportunity

Job summary

loom ai is building continuously learning AI systems for enterprises in regulated industries. You will own the end-to-end ML engineering stack, from data ingestion to deployment and monitoring, using traditional ML tools and modern AI stacks.

You will collaborate with clients and domain experts to ensure models meet business needs with a strong emphasis on safety and governance. This role requires 3–5 years of production ML experience, Python proficiency, and familiarity with RAG, prompts,

Qualifications

• 3–5 years of ML engineering or applied AI with production systems experience.
• Proficiency in Python and traditional ML libraries (LightGBM, XGBoost, scikit-learn) and modern AI stacks (LangChain, LlamaIndex, OpenAI/Anthropic APIs).
• Experience with RAG, prompt engineering, evaluation harnesses, and/or fine-tuning on domain-specific data.
• Familiarity with agent orchestration and systems where models make, route, or compose decisions.
• Experience with full model lifecycle: training, evaluation, shadow, canary, A/B, ongoing tuning.
• Client-facing experience and ability to explain models to non-technical stakeholders.
• Startup mindset: move fast, own your work, and contribute across the stack (product instinct, governable, scalable)

Responsibilities

• Own end-to-end ML engineering stack from data ingestion to deployment and monitoring.
• Build and maintain production AI systems that ingest real enterprise data in real time.
• Develop prompt engineering strategies and evaluation harnesses for model quality.
• Collaborate with clients and domain experts to align AI solutions with business needs.

Skills

Python

ML engineering

Agent orchestration

Production systems

Tools

LightGBM

XGBoost

scikit-learn

LangChain

LlamaIndex

OpenAI/Anthropic APIs

Job description

loom ai is building continuously learning AI systems for enterprises in regulated industries. You will own the end-to-end ML engineering stack, from data ingestion to deployment and monitoring, using traditional ML tools and modern AI stacks.

You will collaborate with clients and domain experts to ensure models meet business needs with a strong emphasis on safety and governance. This role requires 3–5 years of production ML experience, Python proficiency, and familiarity with RAG, prompts,