Senior MLOps Engineer

Job Description:

Responsibilities

• Design, implement, and maintain data pipelines.
• Deploy machine learning models to production environments on AWS,
• ensuring scalability and reliability.
• Implement CI/CD practices for continuous integration and deployment of
• AI models.
• Monitor and optimize the performance of deployed models, using metrics to assess accuracy and effectiveness in real-world applications.

Qualifications & Experience

• 3+ years of commercial hands-on experience with AWS.
• Strong knowledge of Python.
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• In-depth knowledge of AWS services relevant to AI/ML, such as SageMaker, Lambda, EC2, S3, and RDS.
• Experience with Docker and Kubernetes.
• Experience with Kubeflow is a huge plus.
• Familiarity with Vector DBs, LangChain, LangGraph, AI workflows, and RAG systems.
• Understanding in designing, building, and maintaining data pipelines for data ingestion, transformation, and storage.
• Knowledge of ETL processes and tools such as AWS Glue, Apache Airflow, or similar technologies.
• Experience deploying machine learning models on AWS.
• Understanding of CI/CD pipelines for AI model deployment.
• Experience with performance optimization and scaling of AI applications on AWS.
• Strong knowledge of data preprocessing techniques and best practices for managing datasets in cloud environments.
• Experience with databases (SQL, NoSQL) and data warehousing solutions.
• Familiarity with model monitoring tools and techniques on AWS.
• Ability to troubleshoot and maintain deployed models.
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
• At least an upper-intermediate level of English.