Lead AI/ML Engineer: Hybrid AI/Optimization Systems

8451; Cincinnati (OH); On-site; USD 125,000 - 207,000; Full time

Benefits offered by this job

Health plan

401(k) matching

Paid time off

Job summary

84.51° is seeking a Lead AI/ML Engineer to drive optimization‑informed AI solutions on the Labs team. You will hands‑on code, mentor teammates, and collaborate with data scientists to design scalable, production‑grade systems that blend classical optimization with modern ML techniques.

You will shape hybrid architectures, select solvers, and ensure CI/CD and MLOps readiness while partnering with product and business stakeholders to align technical execution with innovation goals.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Applied Mathematics, or a related field
• 4+ years in ML, AI, or optimization with production deployment
• Strong Python coding and software engineering fundamentals
• Experience with PyTorch/TensorFlow/scikit‑learn and Pyomo (Pyomo proficiency required)
• Hands‑on experience deploying optimization problems and solvers such as Gurobi/CPLEX/OR‑Tools

Responsibilities

• Build and maintain end‑to‑end ML, AI, and optimization solutions
• Design hybrid AI/optimization systems with surrogate models and learned heuristics
• Lead design reviews and production‑ready implementation
• Mentor engineers and researchers and collaborate with cross‑functional teams
• Architect robust ML/AI pipelines for experimentation, deployment, and monitoring

Skills

Python

CI/CD

MLOps

Optimization

Team leadership

Cloud platforms

Software engineering

Testing & observability

Education

Bachelor’s or Master’s in Computer Science, ML, Applied Mathematics

Tools

Gurobi

CPLEX

OR-Tools

Pyomo

Docker

Kubernetes

MLflow

Azure ML

Job description

84.51° is seeking a Lead AI/ML Engineer to drive optimization‑informed AI solutions on the Labs team. You will hands‑on code, mentor teammates, and collaborate with data scientists to design scalable, production‑grade systems that blend classical optimization with modern ML techniques.

You will shape hybrid architectures, select solvers, and ensure CI/CD and MLOps readiness while partnering with product and business stakeholders to align technical execution with innovation goals.