Sr. MLOps Engineer

Sr. MLOps Engineer

Location: On-site, or Remote, potentially to Advanced Candidates (no relocation; open to OPT and EAD candidates already in the Columbus area) Reports To: VP of Application Development Focus: Production ML Systems & Deep Learning Platforms

The Role

Plenty of teams can stand up a promising prototype. Far fewer can carry one into production and keep it healthy once real people depend on it. That gap is what this role is built to close. You'll take advanced AI systems out of the lab and into the live environment, tightening delivery, lifting model performance, and keeping things stable where it counts.

The work spans tuning specialized LLMs with reinforcement learning techniques, building synthetic data pipelines that get models production-ready, and designing feedback loops that let models keep improving well after they ship. If turning frontier AI into dependable, scalable products is the kind of problem you want to wake up to, this is your seat.

What You'll Own

Stand up automated, resilient deployment and monitoring frameworks for ML and DL models running at scale

Build sophisticated synthetic data generation workflows that speed up training and real-world readiness

Work with engineering and product leads to turn business needs into secure, scalable AI architectures

Design, train, and optimize deep learning systems, especially transformer-based models and LLMs, using modern reinforcement learning and adaptive fine-tuning

Architect and maintain end-to-end Databricks pipelines: ingestion, feature engineering, MLflow tracking, orchestration, and deployment

Build proofs-of-concept that make the business value obvious and clear a path to production

Own solutions across their full lifecycle, from concept through deployment, governance, monitoring, and iteration

Build automated CI/CD workflows that support continuous model, data, and code evolution

Troubleshoot live AI environments with precision and a steady hand under pressure

Collaborate across AI, data, software, and DevOps teams to keep everyone aligned and shipping

What Success Looks Like

AI systems that hold up in production, both scientifically sound and operationally durable

Deep learning and ML components that fold cleanly into analytics and operational workflows

LLMs tuned for measurable impact, governed well, and delivering real outcomes

Automated, observable pipelines covering the whole path from ingestion to inference

Dependable AI-driven gains across recommendation, language understanding, image interpretation, and beyond

What You Bring

7+ years of professional engineering focused on ML/AI systems - Leading from and MLOps perspective specifically

Bachelor's in a technical field, or the equivalent earned through real-world work

Strong Azure experience and expert-level Databricks across Delta, MLflow, clusters, compute management, and orchestration. In-depth Databricks experience is critical.

Deep learning depth: LLMs, neural architectures, optimization strategies, NLP methods, and evaluation frameworks

Strong Python across prototypes, pipelines, and production codebases

The ability to explain hard technical ideas clearly, tuned to both engineers and business partners

A security-first mindset and comfort in compliance-sensitive environments

Dependability within a 24/7 support rotation and genuine ease working across teams

Curiosity, humility, and an appetite for pushing generative AI further into practical impact