Senior MLOps Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior MLOps Engineer on a contract basis, offering a competitive pay rate. Required qualifications include 8+ years in software/data engineering, 4+ years in MLOps, and expertise in cloud platforms, Python, and ML orchestration tools. Experience in financial services is preferred. Hybrid work in New York or remote for the right candidate.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
-
🗓️ - Date discovered
August 14, 2025
🕒 - Project duration
Unknown
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🏝️ - Location type
Hybrid
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📄 - Contract type
Unknown
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🔒 - Security clearance
Unknown
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📍 - Location detailed
United States
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🧠 - Skills detailed
#Airflow #Lambda (AWS Lambda) #Kubernetes #Monitoring #Scala #AWS (Amazon Web Services) #Deployment #GDPR (General Data Protection Regulation) #TensorFlow #Data Engineering #Data Pipeline #Python #DevOps #Spark (Apache Spark) #Deep Learning #Infrastructure as Code (IaC) #AI (Artificial Intelligence) #SageMaker #Docker #Model Deployment #Databricks #ML (Machine Learning) #Storage #Compliance #Cloud #S3 (Amazon Simple Storage Service) #MLflow #PyTorch #GitHub #Data Science #Jenkins
Role description
Job Title: Senior MLOps Engineer (Contract)
Overview
We are seeking a highly skilled Senior MLOps Engineer to join our data engineering and AI/ML team on a contract basis. This role will focus on designing, implementing, and maintaining robust machine learning infrastructure, enabling scalable model deployment and monitoring in a regulated financial services environment. You will work closely with data scientists, engineers, and cloud architects to ensure operational excellence of ML workflows.
Key Responsibilities
• Design, build, and optimize end-to-end ML pipelines for model training, deployment, and monitoring.
• Implement CI/CD workflows and infrastructure as code for ML projects.
• Collaborate with Data Science teams to productionize models and ensure reproducibility and compliance.
• Manage model lifecycle, including versioning, retraining schedules, and performance monitoring.
• Integrate model governance frameworks to meet financial regulatory requirements.
• Optimize compute and storage resources in the cloud to reduce cost and improve performance.
• Troubleshoot and resolve operational issues across ML systems.
Required Qualifications
• 8+ years in software/data engineering or DevOps, with at least 4+ years in MLOps roles.
• Proven experience with cloud platforms (AWS preferred – SageMaker, EKS, Lambda, S3).
• Strong skills in Python and ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Expertise in containerization and orchestration (Docker, Kubernetes).
• Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, or similar).
• Familiarity with data engineering best practices and data pipelines (Spark, Databricks a plus).
• Knowledge of model monitoring, drift detection, and governance in regulated industries.
• Strong communication skills and ability to work cross-functionally in a hybrid environment.
Preferred Skills
• Experience in financial services or other regulated sectors.
• Understanding of compliance frameworks (e.g., GDPR, OCC, SOX).
• Exposure to deep learning frameworks (TensorFlow, PyTorch).
Work Environment
• Hybrid in New York but open to remote for the right candidate.
• Collaborative, fast-paced environment with high-impact ML initiatives.
Job Title: Senior MLOps Engineer (Contract)
Overview
We are seeking a highly skilled Senior MLOps Engineer to join our data engineering and AI/ML team on a contract basis. This role will focus on designing, implementing, and maintaining robust machine learning infrastructure, enabling scalable model deployment and monitoring in a regulated financial services environment. You will work closely with data scientists, engineers, and cloud architects to ensure operational excellence of ML workflows.
Key Responsibilities
• Design, build, and optimize end-to-end ML pipelines for model training, deployment, and monitoring.
• Implement CI/CD workflows and infrastructure as code for ML projects.
• Collaborate with Data Science teams to productionize models and ensure reproducibility and compliance.
• Manage model lifecycle, including versioning, retraining schedules, and performance monitoring.
• Integrate model governance frameworks to meet financial regulatory requirements.
• Optimize compute and storage resources in the cloud to reduce cost and improve performance.
• Troubleshoot and resolve operational issues across ML systems.
Required Qualifications
• 8+ years in software/data engineering or DevOps, with at least 4+ years in MLOps roles.
• Proven experience with cloud platforms (AWS preferred – SageMaker, EKS, Lambda, S3).
• Strong skills in Python and ML workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow).
• Expertise in containerization and orchestration (Docker, Kubernetes).
• Hands-on experience with CI/CD tools (GitHub Actions, Jenkins, or similar).
• Familiarity with data engineering best practices and data pipelines (Spark, Databricks a plus).
• Knowledge of model monitoring, drift detection, and governance in regulated industries.
• Strong communication skills and ability to work cross-functionally in a hybrid environment.
Preferred Skills
• Experience in financial services or other regulated sectors.
• Understanding of compliance frameworks (e.g., GDPR, OCC, SOX).
• Exposure to deep learning frameworks (TensorFlow, PyTorch).
Work Environment
• Hybrid in New York but open to remote for the right candidate.
• Collaborative, fast-paced environment with high-impact ML initiatives.
