Senior MLOps Engineer

Description 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 Requirements * 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 Preferred Skills: * 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 Job Type Contract Compensation 89 - $120