MLOps Engineer with Data Science & Data Engineering Experience

Job Description:

We are seeking an experienced MLOps Engineer with a strong background in Data Science and Data Engineering to join our team. The ideal candidate will have a solid understanding of machine learning workflows, data pipelines, and cloud infrastructure. The role will focus on implementing and automating ML models and data processing pipelines, ensuring smooth operations, scalability, and performance in a cloud environment.

Key Responsibilities:

• Design, implement, and maintain scalable machine learning pipelines and infrastructure in GCP and AWS.
• Collaborate with data scientists and data engineers to build and deploy ML models into production.
• Optimize ML models for performance and scalability in production environments.
• Automate and streamline data ingestion, data processing, and model deployment workflows using Python, PySpark, and cloud-native tools.
• Develop monitoring systems for deployed models and data pipelines to ensure reliability and uptime.
• Implement CI/CD pipelines for continuous integration and delivery of machine learning models and data workflows.
• Troubleshoot issues and optimize model deployment on cloud infrastructure (AWS and GCP).
• Ensure security and compliance across all deployed models and data workflows.

Required Skills and Experience:

• Strong experience with Python and PySpark for data processing and model development.
• Hands-on experience with cloud platforms, particularly GCP and AWS.
• Expertise in building and maintaining scalable machine learning pipelines and automating ML operations.
• Proficiency in working with data engineering tools for data transformation, ETL processes, and large-scale data handling.
• Experience with CI/CD tools for machine learning models (e.g., Jenkins, GitLab CI/CD).
• Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
• Understanding of containerization and orchestration (e.g., Docker, Kubernetes).
• Experience with model monitoring and logging tools (e.g., Prometheus, Grafana).
• Strong problem-solving skills and ability to work in a collaborative, cross-functional team environment.

Qualifications:

• Bachelor's degree in Computer Science, Data Science, or a related field.
• 3+ years of experience in MLOps, Data Engineering, or a similar role.

Nice to have:

• Experience with big data tools such as Apache Kafka, Hadoop, or Spark.
• Familiarity with data science libraries like TensorFlow, Scikit-learn, or PyTorch.
• Experience with serverless architecture in cloud environments.