AI/ML Engineer

Role Overview

AI/ML Engineer (Contract-to-Hire) working remotely in the US. You will build production AI/ML systems that continuously learn from enterprise decisions, partnering directly with the founders and engaging with clients as a peer.

What You’ll Build

• A continuously learning production decision loop: Read, Act, Observe, Tune
• Multi-engine decision systems running on enterprise data with compliance, auditability, and accuracy requirements
• A full-stack ownership model covering data pipelines, modeling, deployment, and an LLM-powered layer for proposing improvements

Responsibilities

• Own the full ML lifecycle: data pipeline design, training, evaluation, deployment, and continuous tuning
• Design and implement RAG pipelines, evaluation harnesses, and fine-tuning workflows on foundation models
• Architect and orchestrate agentic systems that execute enterprise-scale decisions
• Build and maintain production classifiers (choose between fine-tuned foundation models and gradient-boosted methods)
• Implement shadow/canary/A/B deployment and safe model promotion tooling
• Create ground-truth and verdict frameworks that feed retraining cycles
• Communicate model behavior and confidence scoring to both technical teams and business leaders
• Contribute to the Lumen accelerator by identifying what should be productized and reused across engagements
• Act as a thought partner to founders by challenging assumptions and driving decisions forward

Must-Haves

• 3–5+ years hands-on ML engineering / applied AI experience focused on production systems
• Python proficiency; experience with LightGBM, XGBoost, scikit-learn and modern AI stacks such as LangChain, LlamaIndex, OpenAI/Anthropic APIs
• Proven experience with RAG, prompt engineering, evaluation harnesses, and/or fine-tuning on domain-specific data
• Experience with agent orchestration for routing/combining/deciding
• Ability to design scalable enterprise data pipelines (ingest, normalize, join, persist)
• Strong understanding of model lifecycle management including deployment strategies and ongoing tuning
• Strong client-facing presence and ability to explain model/system behavior to stakeholders
• Startup mentality: fast execution and ownership

Strong Plus

• Salesforce ecosystem experience (text appears truncated: “Agen…”)