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 servingRequirements
* 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