Senior MLOps Engineer

Kforce has a client seeking a Senior MLOps Engineer in Fort Lauderdale, FL to join a high-performing team focused on building and scaling enterprise machine learning platforms. This role is responsible for designing, deploying, and optimizing production-grade ML infrastructure that enables data science teams to efficiently move models from experimentation to production. The ideal candidate has deep experience with Databricks, Apache Spark, Python, and CI/CD practices, along with a strong understanding of the full machine learning lifecycle. This position offers the opportunity to support innovative initiatives involving real-time analytics, recommendation engines, customer personalization, and AI-powered applications. Responsibilities:

• Design, build, and maintain scalable machine learning pipelines on Databricks
• Deploy, monitor, and manage machine learning models in production environments
• Develop and maintain CI/CD pipelines for ML and data workflows
• Build and support batch, streaming, and real-time data pipelines
• Partner with Data Scientists to operationalize and optimize machine learning solutions
• Implement model versioning, experiment tracking, and reproducible ML processes
• Establish and promote ML engineering best practices, governance, and quality standards
• Monitor model performance, data quality, and drift while supporting automated retraining strategies
• Optimize distributed workloads for performance, scalability, and cost efficiency
• Contribute to platform architecture supporting low-latency model inference and scalable model serving
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent experience
• Strong experience with Databricks, including Workflows, MLflow, and Delta Lake
• Advanced expertise with Apache Spark for batch and streaming data processing
• Strong Python development skills with experience building production-quality applications
• Experience designing and implementing CI/CD pipelines for data and machine learning workloads
• Knowledge of machine learning lifecycle management, including training, deployment, monitoring, and retraining
• Experience building scalable and distributed data pipelines and ML systems
• Hands-on experience with real-time or streaming architectures
• Experience working in Azure cloud environments

• Snowflake
• Kubernetes
• Docker
• Terraform or other Infrastructure-as-Code tools
• Feature Store technologies
• Kafka or event-driven architectures
• Model serving frameworks and low-latency API development
• ELK Stack or similar monitoring and observability platforms
• A/B testing and experimentation frameworks
• Large Language Model (LLM) deployment and serving
• RBAC, security, and governance within data and ML platforms