MLOps Engineer

What You Can Expect

Job Summary

The MLOps Engineer is responsible for designing, building, and operating scalable, reliable, and secure machine learning platforms and pipelines. This role bridges data science, software engineering, and cloud infrastructure, enabling models to move from experimentation to production with high availability, governance, and performance.The role focuses on ML platform engineering, automation, reliability, and lifecycle management across training, deployment, monitoring, and retraining of machine learning models.

How You'll Create Impact

Key Responsibilities

ML Platform & Pipeline Engineering

• Design, build, and maintain end-to-end ML pipelines for training, validation, deployment, and monitoring
• Productionize machine learning models developed by Data Scientists
• Implement standardized workflows for feature engineering, model versioning, and model promotion

Deployment, Monitoring & Reliability

• Deploy models using containerized and cloud-native architectures
• Implement monitoring for model performance, data drift, and system health
• Lead root-cause analysis for model or pipeline failures and implement long-term fixes

Automation & DevOps for ML

• Build CI/CD pipelines for ML workflows (training, testing, deployment)
• Automate infrastructure provisioning and environment management
• Enforce repeatability, reproducibility, and traceability of ML experiments

Governance, Security & Compliance

• Implement ML governance controls including lineage, auditability, and access control
• Partner with Security, GRC, and Data Governance teams to ensure compliance
• Support responsible AI practices and enterprise standards

Collaboration & Enablement

• Partner closely with Data Scientists, Data Engineers, and Platform Engineers
• Provide guidance and best practices for scalable model development
• Contribute to documentation, standards, and internal enablement

What Makes You Stand Out

Technologies & Tools

Machine Learning & MLOps

• Python (primary), with ML libraries (scikit-learn, TensorFlow, PyTorch – support level)
• MLflow, Kubeflow, or similar ML lifecycle tools
• Feature stores (e.g., Feast, cloud-native feature stores)
• Model registries and experiment tracking

Data & Pipeline Engineering

• Workflow orchestration tools (e.g., Airflow, Dagster, Prefect)
• Data processing frameworks (Spark, distributed data processing concepts)
• SQL and data warehousing fundamentals

Cloud & Infrastructure

• Cloud platforms: AWS, Azure, or GCP (at least one)
• Containerization: Docker
• Orchestration: Kubernetes
• Infrastructure as Code: Terraform, ARM/Bicep, or CloudFormation

DevOps & CI/CD

• CI/CD tools (GitHub Actions, GitLab CI, Azure DevOps, Jenkins)
• Version control: Git
• Monitoring and logging (Prometheus, Grafana, Cloud-native monitoring tools)

Security & Governance

• Identity and access management (RBAC, secrets management)
• Data privacy and model governance concepts
• Exposure to regulated or SOX-controlled environments (preferred)

Your Background

Required Qualifications

Education

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience)

Years of Experience

• 5–8 years of experience in software engineering, data engineering, or platform engineering
• 3+ years of hands-on experience in MLOps, ML platform engineering, or ML deployment
• Experience supporting production-grade machine learning systems

Preferred Qualifications

• 7+ years total engineering experience
• Experience supporting real-time or near-real-time ML inference
• Experience with model monitoring, drift detection, and retraining automation
• Experience working in enterprise or regulated environments
• Certifications in cloud platforms or data/ML engineering (preferred)

Core Competencies

• Strong systems and platform engineering mindset
• Advanced troubleshooting and problem-solving skills
• Ability to translate research models into reliable production systems
• Clear communication across Data Science, Engineering, and IT
• Strong ownership for reliability, scalability, and security
• Experience operating in cloud-based, distributed environments

Physical Requirements

Travel Expectations