Senior MLOps Engineer (AI)

Location: Chattanooga, Tennessee, USA

Base salary: $89,400.00–$183,500.00 per year

Company

hirify.global is a Fortune 500 employee benefits provider helping people access benefits that protect their families, finances, and futures.

What you will do

• Design, build, maintain, and scale production ML pipelines and platforms for model deployment, serving, monitoring, and lifecycle management.
• Build and improve CI/CD workflows with automated testing, versioning, deployment, and release processes.
• Develop and operate infrastructure for real-time and batch model inference using Infrastructure as Code.
• Implement observability for model performance, system health, reliability, and operational issues.
• Expand MLOps capabilities into GenAI and AgentOps, including evaluation, versioning, monitoring, tracing, cost management, and guardrails.
• Partner with data scientists and engineers, establish engineering standards, contribute to technical decisions, and mentor engineers.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Information Systems, a related field, or equivalent relevant work experience.
• 5+ years of experience in software engineering, DevOps, ML engineering, MLOps, or a related discipline, including experience supporting machine learning systems in production.
• Strong Python programming skills and software engineering fundamentals.
• Hands-on production MLOps experience with CI/CD, model deployment, model serving, and monitoring.
• Hands-on experience with AWS and cloud-based infrastructure, Docker, Kubernetes, and Infrastructure as Code such as Terraform.
• Hands-on experience contributing to at least one LLM or agentic AI project, including a proof of concept, pilot, or production implementation. Relevant areas include RAG, evaluation, versioning, monitoring, tracing, cost management, or guardrails.
• Experience with ML lifecycle and platform technologies, with comparable production MLOps tools and platforms considered.
• Experience building or maintaining CI/CD pipelines and observability or monitoring technologies.
• Strong communication and collaboration skills across data science and engineering teams, plus the ability to mentor engineers and influence technical direction without formal people-management responsibility.

Nice to have

• Experience with Databricks, including Unity Catalog, Jobs/Workflows, Model Serving, and Lakehouse Monitoring.
• Experience with MLflow for ML lifecycle management.
• Experience supporting GenAI or agentic AI solutions beyond initial experimentation.
• Experience with AI or agent observability, evaluation, tracing, versioning, cost management, or guardrails.
• Background as a software or DevOps engineer who transitioned into MLOps, or as an ML engineer with strong infrastructure engineering experience.
• Experience owning or improving an ML platform at scale.
• Experience with GitHub Actions, AWS CloudWatch, or Databricks Lakehouse Monitoring.

Culture & Benefits

• Agile, fast-paced, delivery-focused environment with an entrepreneurial spirit and emphasis on challenging the status quo.
• Healthcare, vision, dental, disability, and insurance benefits.
• Paid time off and onsite fitness facilities.
• Professional development programs.
• Performance-based incentive plans and a 401(k) retirement plan with employer contributions.