Artificial Intelligence Engineer

Opportunity Engine, Inc.; Chicago (IL); Sur place; USD 140 000 - 210 000; Plein temps

Résumé du poste

Opportunity Engine, Inc. seeks an AI Engineer to build the AI, ML, and agentic systems behind a regulatory-tech product that analyzes laws and turns them into actionable guidance. You will own the full lifecycle from data and experimentation to evaluation, deployment, and real-world performance, working with product and engineering to ship features.

Strong candidates enjoy solving hard problems, building robust systems, and turning promising models into practical products in a fast-paced startup.

Qualifications

• Hands-on experience training, tuning, and shipping AI/ML models in production.
• Experience owning the ML lifecycle, including data collection, labeling, training, deployment, and monitoring.
• Strong evaluation skills and the ability to diagnose model failures in real-world conditions.
• A commitment to robust engineering, observability, tooling, and efficient development workflows.
• Comfort with ambiguity, changing requirements, pragmatic tradeoffs, and taking ownership in a fast-paced environment.
• Ability to translate business needs into ML solutions and explain model behavior to non-technical stakeholders.
• Azure ML or similar cloud platform experience is a plus; NLP, document understanding, or classification experience is a bonus.

Responsabilités

• Build, train, evaluate, and deploy AI/ML and agentic systems across the full lifecycle.
• Develop data pipelines, curate representative datasets, and improve models through rapid experimentation.
• Design rigorous evaluations and use production feedback to improve real-world performance.
• Deploy reliable, low-latency AI workloads in Azure, including monitoring, versioning, and retraining pipelines.
• Partner with product and engineering to deliver user-facing AI features and contribute to backend development.

Connaissances

AI/ML model training

ML lifecycle ownership

Model evaluation

Observability & tooling

Ambiguity tolerance

Business-to-ML translation

Outils

Azure ML

Cloud platforms

Description du poste

Our client is a venture-backed legal AI startup helping organizations turn regulatory change into clear, practical action. Its platform monitors evolving laws and regulations, identifies what matters to each business, and highlights gaps in existing compliance programs.

Join a team where your ideas can shape the product and your work can make a direct impact. The culture emphasizes ownership, meaningful results, resourcefulness, and open debate - bringing together the urgency of a startup with a commitment to building reliable AI.

ABOUT THE ROLE

We are seeking an AI Engineer to build the AI, machine learning, and agentic systems behind a product tackling complex regulatory challenges. This is an opportunity to own the full lifecycle - from data and experimentation to evaluation, deployment, and real-world performance.

You'll work closely with product and engineering, ship quickly, and see your work become features customers use. If you enjoy solving difficult problems, building robust systems, and turning promising models into practical products, this role offers room to take ownership and help shape what comes next.

RESPONSIBILITIES:

• Build, train, evaluate, and deploy AI/ML and agentic systems across the full lifecycle.
• Develop data pipelines, curate representative datasets, and improve models through rapid experimentation.
• Design rigorous evaluations and use production feedback to improve real-world performance.
• Deploy reliable, low-latency AI workloads in Azure, including monitoring, versioning, and retraining pipelines.
• Partner with product and engineering to deliver user-facing AI features and contribute to backend development.

QUALIFICATIONS:

• Hands-on experience training, tuning, and shipping AI/ML models in production.
• Experience owning the ML lifecycle, including data collection, labeling, training, deployment, and monitoring.
• Strong evaluation skills and the ability to diagnose model failures in real-world conditions.
• A commitment to robust engineering, observability, tooling, and efficient development workflows.
• Comfort with ambiguity, changing requirements, pragmatic tradeoffs, and taking ownership in a fast-paced environment.
• Ability to translate business needs into ML solutions and explain model behavior to non-technical stakeholders.
• Azure ML or similar cloud platform experience is a plus; NLP, document understanding, or classification experience is a bonus.