MLOps Engineer

We are working on a brand-new project, which is an Innovation Hub/Ai Lab. Job Purpose Designs and develops a ML platform to empower the Data Science and AI team to scale ML experimentation and speed up path to production.

Key Responsibilities

• Design and automate robust ML pipelines for continuous integration, continuous delivery, and continuous training (CI/CD/CT) of models
• Configure model serving architectures to support both ultra-low latency real-time APIs (e.g., FastAPI, gRPC) and large-scale batch inference
• Implement model monitoring and logging systems to track model metrics, runtime latencies, data drift, and concept decay in production
• Manage model registries and metadata to ensure reproducible versioning, tracking, and auditable lineage of ML artifacts (using MLflow, Kubeflow, etc.)
• Orchestrate and scale containerized applications using Docker and production-grade Kubernetes or managed container services (EKS, AKS)
• Collaborate with security and data teams to enforce strict data governance, pipeline encryption, network isolation, and secure IAM policies
• Desired Candidate Profile 3-7) years of experience building and operating high-availability ML pipelines, automation scripts, and foundational platform infrastructure layers
• Strong programming skills in Python, Go, or Java alongside systems scripting and deep hands-on familiarity with Linux environments
• Deep familiarity with MLOps tools such as MLflow, Kubeflow, Argo Workflows, Feast, or managed platform equivalents (SageMaker, Azure ML)
• Understanding of cloud security architectures including VPC isolation, private endpoints, encryption at rest/in transit, and role-based access control
• Understanding of containerization and orchestration using Docker and Kubernetes, alongside Infrastructure-as-Code (Terraform, CloudFormation)