MLOps Engineer

About The Role

The role bridges the gap between machine learning research and production engineering, focusing on the automation and scaling of model lifecycles. This position ensures that complex AI systems are not just accurate in a notebook, but robust, observable, and cost-efficient when serving live traffic in a high-scale environment.

The team focuses on building the foundational infrastructure that enables data scientists to deploy and monitor models with minimal friction. This includes developing automated CI/CD pipelines for ML, managing high-throughput inference services, and architecting unified data layers for real-time feature serving.

Key Responsibilities

• Build and maintain automated CI/CD pipelines specifically for machine learning models, ensuring seamless transitions from experimentation to production environments
• Architect and manage Kubernetes-based inference clusters to serve low-latency model predictions using tools like KServe, BentoML, or NVIDIA Triton
• Implement comprehensive monitoring and observability stacks to detect data drift, concept drift, and performance degradation in real-time production models
• Design and optimize feature stores and data pipelines using Spark, Flink, or Feast to ensure training-serving consistency across the organization
• Manage infrastructure as code (IaC) using Terraform or Pulumi to maintain reproducible ML environments across AWS or GCP cloud providers
• Collaborate with security and data engineering teams to implement strict data governance, access controls, and lineage tracking for all production model artifacts

What We Are Looking For

• 4-7 years of experience in DevOps, SRE, or Software Engineering, with at least 2 years focused specifically on MLOps and production ML systems
• Advanced proficiency in Python and containerization technologies, specifically Docker and Kubernetes (K8s)
• Hands-on experience with ML orchestration tools such as Kubeflow, Airflow, or Metaflow for complex workflow management
• Strong understanding of the ML lifecycle including versioning data (DVC), tracking experiments (MLflow/Weights \& Biases), and model registries
• Solid foundation in cloud networking, distributed systems, and scalable database architecture (SQL and NoSQL)
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field
• Bonus: Experience with LLM-specific operations (LLMOps) including vector database management and GPU resource optimization