Lead

Fractal Analytics is Leading Fortune 500 companies leverage Big Data, analytics, and technology to drive smarter, faster and more accurate decisions in every aspect of their business. Fortune 500 companies recognize analytics is a competitive advantage to understand customers and make better decisions. We deliver insight, innovation, and impact to them through predictive analytics and visual storytelling. 

Role 

We are seeking a Lead MLOps Engineer to design, build, and scale machine learning platforms and production systems. This role focuses on enabling end-to-end ML lifecycle management, ensuring scalability, reliability, and governance across model development and deployment. 

Key Responsibilities 

• Design, implement, and maintain scalable machine learning pipelines. 

• Build and manage ML workflows using tools such as Domino Data Lab, AWS SageMaker, and MLflow. 

• Collaborate with data scientists and engineers to deploy models into production. 

• Monitor model performance and ensure reliability, scalability, and reproducibility. 

• Automate model training, testing, and deployment using CI/CD practices. 

• Implement model versioning, experiment tracking, and lineage management. 

• Deploy and manage large-scale GLM/LLM models using efficient serving frameworks. 

• Ensure compliance with data governance, security, and regulatory standards. 

• Drive containerization and infrastructure automation practices. 

• Mentor team members and drive MLOps best practices across teams. 

Required Skills & Experience 

• Strong experience in MLOps and ML lifecycle management. 

• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn). 

• Hands-on experience with Domino Data Lab, AWS SageMaker, and MLflow. 

• Strong understanding of core machine learning concepts. 

• Expertise in containerization (Docker, Kubernetes) and model packaging. 

• Experience in deploying large GLM/LLM models and serving frameworks. 

• Familiarity with model serialization formats (ONNX, Pickle, TorchScript). 

• Knowledge of CI/CD tools and cloud infrastructure. 

• Excellent problem-solving and communication skills. 

Nice to Have 

• Experience with streaming data pipelines (Kafka, Spark). 

• Exposure to Responsible AI practices. 

• Knowledge of infrastructure as code (Terraform, CloudFormation).