Senior MLOps Engineer

Introduction & Summary:

We are seeking a Senior MLOps Engineer who will play a critical role in optimizing the lifecycle of machine learning models. The ideal candidate will possess proven expertise in MLOps or DevOps practices, along with strong skills in automation and cloud platforms. This role emphasizes engineering, data security, and collaboration with data science teams to enhance model productivity and reliability.

You will be working on an internal emagine platform offering AI services to both internal and external clients. Its critical you are a “do’er” and very self dependant.

Main Responsibilities:

• Design, build, and maintain scalable and automated CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for machine learning models.

• Automate the provisioning and management of infrastructure across a hybrid environment (Azure, GCP, On-premise).

• Collaborate with data scientists to streamline model development, containerization, and deployment.

• Implement and manage robust data security and governance practices, ensuring the protection of sensitive data throughout the entire ML lifecycle.

• Manage various data storage solutions, including SQL, vector, and graph databases, to support different ML model requirements.

• Implement monitoring solutions to track model performance, data drift, and the health of the underlying infrastructure.

• Integrate ML platforms and tools, such as Hugging Face, into our workflows to accelerate model development and deployment.

Key Requirements:

• Proven experience in an MLOps, DevOps, or similar infrastructure automation role.

• Strong proficiency in automation and scripting languages (e.g., Python, Bash).

• Solid understanding of containerization technologies, particularly Docker and Kubernetes.

• Experience working with cloud platforms, with a strong preference for GCP (especially its ML services) and Azure.

• A deep understanding of data security principles and best practices for protecting data in transit and at rest.

• Proficiency in fundamental networking protocols and concepts.

• Familiarity with a variety of database systems, including relational (SQL), vector, and graph databases.

• Basic programming skills in Python.

• A solid conceptual understanding of modern AI/ML technologies, including LLMs, RAG (Retrieval-Augmented Generation), and embeddings.

Nice to Have:

• Advanced proficiency in Python and experience with common ML libraries such as Transformers, PyTorch, or TensorFlow.

• Hands-on experience with specific GCP ML services (e.g., Vertex AI, BigQuery ML).

• Experience managing on-premise infrastructure for GPU-intensive workloads.

• Demonstrated experience in building and maintaining hybrid-cloud environments.

Other Details:

This role offers the opportunity to work within a dynamic and innovative environment, contributing to impactful machine learning projects across various platforms. Preferred candidates should have experience in hybrid-cloud infrastructure management.