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KT
Kforce Technology Staffing
Senior MLOps Engineer
Career Insights for Machine Learning Engineer
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Based on Florida data
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What they do
A Machine Learning Engineer specializes in designing, building, and deploying machine learning models. They utilize statistical and mathematical techniques, parallelizing processing, hyperparameter tuning, and other optimization methodologies to improve model performance. Responsibilities also include collecting and preprocessing large datasets, conducting exploratory data analysis, working closely with data engineers to understand data requirements, and engineer input variables for machine learning models.
$127,022 / year median in Florida
Job Description
Senior MLOps Engineer Fort Lauderdale, FL, US
- Posted 3 days ago
- Updated 1 hour ago Contract W2 On-site $89.
RESPONSIBILITIES
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 The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role.