Job Title: Data Engineer + Data Scientist (GenAI MLOps Engineer (Machine Learning Operations) 
Job type: 12 months contract
Job Location: Jersey City, NJ
Job Overview: 
We are seeking a highly skilled and innovative GenAI MLOps Engineer to join our dynamic data engineering and data science team. As an MLOps Engineer, you will bridge the gap between data engineering, machine learning, and cloud operations, focusing on scalable and efficient deployment, monitoring, and management of machine learning models in a cloud environment, primarily on AWS. 

Key Responsibilities: 

• MLOps Automation & Deployment: 
• Design, implement, and maintain ML pipelines and automation frameworks for model deployment and monitoring. 
• Leverage AWS services such as SageMaker, Lambda, EC2, ECS, and EKS for scalable ML model deployment and integration. 
• Collaborate with data scientists and ML engineers to convert ML models into production-ready solutions. 
• Continuously optimize and monitor model performance, including A/B testing, model versioning, and model retraining. 
• Palentir Experience: 
• Strong hands-on experience working with Palantir Foundry and/or Palantir Gotham for data integration, transformation, and analysis. 
• Design and implement automated ML pipelines to support the deployment, monitoring, and management of machine learning models using Palantir Foundry, Palantir Gotham, and AWS. 
• Utilize Palantir Foundry for managing end-to-end data pipelines and operationalizing machine learning solutions at scale. 
• Apply Palantir Cloud and other cloud-native tools to manage data governance, compliance, and security for machine learning models and large datasets. 
• Data Engineering: 
• Build robust data pipelines to support training, testing, and inference of machine learning models. 
• Develop and manage ETL processes using AWS services such as Glue, Redshift, Kinesis, and S3. 
• Design and manage data architecture to ensure high availability, security, and efficient processing of large datasets. 
• Machine Learning & Data Science Collaboration: 
• Work closely with the data science team to translate data science models into production-ready systems. 
• Assist in model selection, optimization, and testing for performance and scalability. 
• Conduct model performance monitoring and facilitate ongoing improvements based on feedback and data analysis. 
• Cloud Infrastructure & Security: 
• Architect and maintain scalable, reliable, and secure infrastructure for machine learning workloads on AWS. 
• Manage infrastructure as code using tools like CloudFormation or Terraform. 
• Implement best practices for data security, compliance, and model governance. 
• Collaboration & Documentation: 
• Collaborate with cross-functional teams to define system requirements and performance metrics. 
• Provide documentation for the MLOps pipeline, data workflows, and models in production. 
• Train and mentor junior team members in MLOps best practices and tools. 

Required Skills and Qualifications: 

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, or a related field. 
• Strong hands-on experience working with Palantir Foundry and/or Palantir Gotham for data integration, transformation, and analysis. 
• Proven experience in MLOps or similar roles involving machine learning and cloud operations. 
• Strong knowledge of AWS services (e.g., SageMaker, Lambda, EC2, S3, Redshift, Glue, Kinesis). 
• Proficiency in programming languages such as Python, SQL, and bash for data manipulation and automation. 
• Experience with machine learning frameworks and tools like TensorFlow, PyTorch, Scikit-learn, MLflow, and Kubeflow. 
• Familiarity with containerization and orchestration technologies such as Docker and Kubernetes. 
• Solid understanding of CI/CD pipelines and tools like Jenkins, GitLab, or GitHub Actions. 
• Strong problem-solving skills with the ability to troubleshoot and debug complex systems. 
• Excellent communication skills, both written and verbal, with the ability to present technical concepts clearly. 

Preferred Qualifications: 

• Advanced degree (Master’s or Ph.D.) in a relevant field. 
• AWS Certified Solutions Architect or Certified Machine Learning Specialty certification. 
• Familiarity with DevOps practices and tools. 
• Experience with real-time data streaming and event-driven architectures. 
• Knowledge of big data technologies like Hadoop, Spark, and Flink. 

Desired Personal Attributes: 

• Strong curiosity and eagerness to learn new technologies and trends in AI and cloud computing. 
• Self-starter with the ability to work independently and take ownership of projects. 
• Collaborative team player with a passion for delivering high-quality solutions. 
• Attention to detail, particularly in managing complex systems and workflows.

Job Types: Full-time, Contract
Pay: $116,582.00 - $124,380.00 per year

Benefits:

• Health insurance

Schedule:

• Day shift

Application Question(s):

• How many years of experience do you have as GenAI MLOps Engineer?

Work Location: In perso

• MLOps Automation & Deployment: 
• Design, implement, and maintain ML pipelines and automation frameworks for model deployment and monitoring. 
• Leverage AWS services such as SageMaker, Lambda, EC2, ECS, and EKS for scalable ML model deployment and integration. 
• Collaborate with data scientists and ML engineers to convert ML models into production-ready solutions. 
• Continuously optimize and monitor model performance, including A/B testing, model versioning, and model retraining. 
• Palentir Experience: 
• Strong hands-on experience working with Palantir Foundry and/or Palantir Gotham for data integration, transformation, and analysis. 
• Design and implement automated ML pipelines to support the deployment, monitoring, and management of machine learning models using Palantir Foundry, Palantir Gotham, and AWS. 
• Utilize Palantir Foundry for managing end-to-end data pipelines and operationalizing machine learning solutions at scale. 
• Apply Palantir Cloud and other cloud-native tools to manage data governance, compliance, and security for machine learning models and large datasets. 
• Data Engineering: 
• Build robust data pipelines to support training, testing, and inference of machine learning models. 
• Develop and manage ETL processes using AWS services such as Glue, Redshift, Kinesis, and S3. 
• Design and manage data architecture to ensure high availability, security, and efficient processing of large datasets. 
• Machine Learning & Data Science Collaboration: 
• Work closely with the data science team to translate data science models into production-ready systems. 
• Assist in model selection, optimization, and testing for performance and scalability. 
• Conduct model performance monitoring and facilitate ongoing improvements based on feedback and data analysis. 
• Cloud Infrastructure & Security: 
• Architect and maintain scalable, reliable, and secure infrastructure for machine learning workloads on AWS. 
• Manage infrastructure as code using tools like CloudFormation or Terraform. 
• Implement best practices for data security, compliance, and model governance. 
• Collaboration & Documentation: 
• Collaborate with cross-functional teams to define system requirements and performance metrics. 
• Provide documentation for the MLOps pipeline, data workflows, and models in production. 
• Train and mentor junior team members in MLOps best practices and tools.