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Senior Machine Learning Data Engineer
2 months ago
We are a forward-thinking, dynamic tech company dedicated to leveraging cutting-edge machine learning and data engineering solutions to solve real-world challenges. Our company culture is built on innovation, collaboration, and growth.
Job OverviewWe are seeking an experienced and results-oriented Senior Machine Learning Data Engineer to play a critical role in architecting, building, and maintaining our data infrastructure and machine learning pipelines. In this role, you will work closely with cross-functional teams, including Data Scientists, Software Engineers, and Product Managers, to ensure efficient data workflows and implement robust machine learning models in production.
Key Responsibilities- Data Pipeline Architecture: Design, develop, and optimize scalable, reliable data pipelines and ETL processes to support machine learning and analytics initiatives.
- Model Deployment: Collaborate with data scientists to deploy machine learning models into production environments, ensuring performance, scalability, and reliability.
- Data Infrastructure: Build and maintain large-scale distributed systems, including data lakes, warehouses, and real-time data streaming solutions.
- Machine Learning Workflows: Develop, automate, and manage machine learning workflows, from model training and validation to deployment and monitoring.
- Collaboration: Work closely with data scientists and other engineers to ensure seamless integration between data pipelines and machine learning models.
- Performance Tuning: Continuously improve data processing performance, ensuring data quality, and optimizing resources in the cloud or on-premise environments.
- Mentorship & Leadership: Provide technical mentorship to junior engineers and lead complex projects that drive the organization's data strategy forward.
- Tech Stack Mastery: Stay current with the latest trends and technologies in machine learning, big data, and distributed computing, and apply them to solve engineering challenges.
- 5+ years of experience in data engineering, machine learning engineering, or a similar field.
- Proven expertise in designing and managing ETL pipelines, data lakes, and data warehouses.
- Strong programming skills in Python and proficiency in SQL.
- Extensive experience with cloud platforms such as AWS, GCP, or Azure.
- Hands-on experience with big data tools such as Apache Spark, Kafka, and Hadoop.
- Knowledge of ML frameworks and libraries such as TensorFlow, PyTorch, or Scikit-Learn.
- Experience deploying and managing machine learning models in production using ML Ops frameworks (e.g., MLflow, Kubeflow, SageMaker).
- Strong understanding of data modeling, data warehousing, and distributed computing concepts.
- Excellent problem-solving skills with a strong emphasis on performance and scalability.
- Ability to work autonomously and lead complex projects end-to-end.
- Experience with real-time data processing and streaming architectures.
- Familiarity with containerization and orchestration tools like Docker and Kubernetes.
- Experience in CI/CD pipelines for data and ML workflows.
- Knowledge of DevOps practices for ML and data engineering systems.
- Competitive contract day rate and potential for long-term collaboration.
- Flexible working environment (Remote/Hybrid options available).
- Opportunity to work on innovative, cutting-edge technologies with a passionate team.
- Professional development opportunities and access to industry-leading tools and resources.
