Staff ML Ops Engineer

Ybor Technology, LLC; Tampa (FL); Remote; USD 150,000 - 750,000; Full time

Job summary

An innovative firm is seeking a seasoned Staff ML Ops Engineer to architect and optimize an ML inference platform. This role focuses on building scalable ML systems and requires deep expertise in Machine Learning engineering and infrastructure. The ideal candidate will lead MLOps strategies, collaborate with teams, and ensure efficient model workflows. Join a forward-thinking company that fosters a culture of innovation and technical excellence, empowering you to tackle complex challenges in ML Ops and AI infrastructure. If you’re passionate about driving real-world impact through AI, this opportunity is perfect for you.

Qualifications

• 5+ years of experience in designing and implementing scalable ML inference systems.
• Strong background in ML frameworks and distributed computing.
• Experience with model optimization techniques and governance frameworks.

Responsibilities

• Lead the MLOps platform ensuring scalable and efficient model workflows.
• Develop and optimize ML pipelines to support model performance.
• Collaborate with cross-functional teams to align MLOps strategies.

Skills

Machine Learning engineering

MLOps

Python programming

Problem-solving skills

Cloud platforms (Azure, GCP, AWS)

CI/CD experience

Database systems (SQL, NoSQL)

Model optimization techniques

Education

Degree in Computer Science

Advanced certifications in cloud computing

Tools

TensorFlow

PyTorch

Scikit-learn

Docker

Kubernetes

MLflow

Kubeflow

SageMaker

Job description

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We are seeking a seasoned Staff ML Ops Engineer to architect, build, and optimize an ML inference platform including agentic AI workflows. This role requires deep expertise in Machine Learning engineering and infrastructure, with a primary focus on building and scaling ML inference systems in production. The ideal candidate will have proven experience in designing scalable, reliable ML pipelines and working independently on complex challenges with innovative solutions, with a mindset for automation.

Responsibilities

• Lead the MLOps platform, ensuring scalable, reliable, and efficient model workflows in production.
• Develop and optimize ML pipelines to support model performance and scalability across the organization.
• Design and implement high-performance inference systems for a variety of models, Agentic AI workflows with purpose driven LLMs integration.
• Collaborate with cross-functional teams (Data Science, Software Engineering, and Product) to align MLOps strategies with business goals.
• Provide technical leadership and mentorship, guiding best practices for MLOps and DevOps.
• Automate and streamline deployment of ML models to production, ensuring minimal downtime and robust versioning.
• Develop and integrate CI/CD workflows for ML systems.
• Implement monitoring tools to track model and system performance.
• Ensure compliance, security, and governance in ML workflows and deployments.
• Analyze cost-performance trade-offs to optimize resource allocation and system efficiency.
• Communicate machine learning engineering strategies effectively across different management levels and stakeholders.

Requirements

• Proven experience in designing and implementing scalable ML inference systems.
• Strong background in ML frameworks (TensorFlow, PyTorch, Scikit-learn) and distributed computing.
• Expertise in cloud platforms (Azure, GCP or AWS) and containerization (Docker, Kubernetes).
• Strong CI/CD experience (GitHub Actions, ArgoCD).
• Hands-on experience with one or more model deployment technologies (MLflow, Kubeflow, Seldon, SageMaker).
• Advanced programming skills in Python; experience in Java, Scala, or Go is a plus.
• Experience with database systems (SQL, NoSQL) and big data frameworks (Spark, Hadoop) is a plus.
• Strong grasp of ML Ops capabilities including Data Versioning, Feature Store, Model Monitoring, and Experiment Tracking.
• Experience with model optimization techniques such as distillation, quantization, and hardware acceleration.
• Familiarity with governance frameworks for responsible AI is preferred.
• Strong problem-solving skills and the ability to work independently in a remote setting.

Qualifications

• Degree in Computer Science with 5+ years of relevant industry experience, specialized in Machine Learning.
• Advanced certifications in cloud computing, machine learning, or DevOps is a big plus.

About Ybor.ai

At Ybor.ai, we are at the forefront of building enterprise solutions infusing AI that drive real-world impact. Our multi-cloud platform serves to provision, compute, connect data, infuse AI and rapidly deploy enterprise workloads to any cloud. We foster a culture of innovation, collaboration, and technical excellence, empowering our engineers to push the boundaries of ML Ops and AI infrastructure.

Join us to lead and shape the future of ML inference at scale!

#MLOps #MachineLearning #AI #DeepLearning #MLInfrastructure #Hiring #TechJobs #RemoteJobs

Seniority level

• Seniority level

  Mid-Senior level

Employment type

• Employment type

  Full-time

Job function

• Job function

  Engineering and Information Technology

• Industries

  Software Development

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