We are seeking a highly skilled MLOps Engineer to join an Enterprise Data & Analytics team supporting the full lifecycle of AI/ML development through production and beyond
Key Responsibilities
Define scalable and secure architectures, frameworks, and pipelines for ML applications
Design and implement cloud-based MLOps pipelines (AWS preferred)
Build and enhance self-service ML development tooling
Automate testing, validation, and deployment of ML models
Perform containerization, deployment, versioning, monitoring, and drift detection
Collaborate with engineers and Scrum teams to create user stories and technical tasks
Troubleshoot platform issues and maintain clear documentation
Develop standards and best practices to accelerate data science productivity
Required Qualifications
Bachelor’s degree with 8+ years experience OR Master’s degree with 6+ years experience
8+ years experience in object-oriented programming (Python, Golang, Java, C/C++, etc.)
Strong proficiency in Python, R, SQL
Experience with MLOps frameworks (MLflow, Kubeflow, etc.)
Strong knowledge of DevOps principles and CI/CD tools (Git, GitHub, Artifactory, Azure DevOps, etc.)
Experience with Docker & Kubernetes
Experience designing and implementing cloud solutions (AWS preferred)
Strong collaboration and communication skills
Nice to Have
Experience building model inference systems integrated with MLflow
Knowledge of Seldon, Kubeflow inference systems
Experience deploying with Helm / Helmfile
Infrastructure as Code (Terraform or CloudFormation)
Exposure to observability tools (e.g., Evidently AI)
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