We are looking for an experienced MLOps Engineer to design, build, automate, and maintain scalable infrastructure for AI/ML applications and data science platforms. The ideal candidate will have strong expertise in cloud infrastructure, DevOps automation, CI/CD pipelines, Kubernetes, containerization, infrastructure as code, and end-to-end machine learning lifecycle management.
The candidate will collaborate closely with Data Scientists, Machine Learning Engineers, AI Engineers, and Platform Engineering teams to operationalize machine learning models, automate model deployment, ensure production reliability, and establish robust monitoring, security, and governance practices.
Experience in healthcare, life sciences, clinical data, or regulated environments is highly desirable.
Design, implement, and maintain scalable cloud infrastructure for AI/ML workloads and data science platforms.
Build and manage end-to-end MLOps pipelines covering model development, training, validation, deployment, monitoring, and retraining.
Develop and maintain CI/CD/CT pipelines for application code, ML models, data pipelines, and infrastructure.
Automate infrastructure provisioning and configuration using Terraform, CloudFormation, or similar Infrastructure as Code (IaC) tools.
Deploy and manage containerized applications and ML workloads using Docker and Kubernetes.
Implement model versioning, experiment tracking, model registry, artifact management, and reproducible ML workflows.
Work with MLflow and related tools to manage the machine learning lifecycle.
Integrate ML pipelines with cloud-native services, data platforms, feature stores, and model-serving infrastructure.
Implement automated testing, deployment strategies, rollback mechanisms, and release management processes.
Establish observability through logging, metrics, tracing, infrastructure monitoring, and model performance monitoring.
Optimize infrastructure utilization, scalability, availability, performance, and cloud costs.
Implement security controls, IAM, secrets management, network policies, and compliance requirements.
Troubleshoot production infrastructure, deployment failures, pipeline issues, and model-serving problems.
Collaborate with Data Scientists, ML Engineers, AI Engineers, and DevOps teams to move models from experimentation to production.
Support reliable and auditable deployment practices in regulated healthcare and life sciences environments.
1. Cloud Infrastructure
AWS, Microsoft Azure, or Google Cloud Platform (Google Cloud Platform)
Cloud networking, IAM, storage, compute, and managed Kubernetes services
Cloud security, scalability, high availability, and cost optimization
2. DevOps and CI/CD
Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps
Git, automated build and release pipelines
Continuous Integration and Continuous Deployment
Automated testing, release automation, and rollback strategies
3. Infrastructure as Code and Configuration Management
Terraform
AWS CloudFormation, Ansible, or equivalent tools
Infrastructure provisioning, configuration automation, and environment management
4. Containers and Orchestration
Docker
Kubernetes
Helm
Container registries and deployment orchestration
Production workload management and scaling
5. MLOps and ML Lifecycle Management
MLflow for experiment tracking, model registry, and lifecycle management
End-to-end model training and deployment pipelines
Model versioning, reproducibility, and artifact management
Model serving, monitoring, drift detection, and retraining automation
Experience integrating Python-based ML workflows into production environments
6. Programming and Automation
Python
Bash or Shell scripting
YAML and configuration management
REST APIs and automation scripts
7. Monitoring and Observability
Prometheus and Grafana
ELK/EFK stack, CloudWatch, or equivalent monitoring platforms
Application and infrastructure logging, alerting, and performance monitoring
Experience supporting AI/ML, Generative AI, or data science workloads in production.
Familiarity with Kubeflow, Airflow, or similar ML/data pipeline orchestration tools.
Knowledge of feature stores, model-serving frameworks, and distributed training infrastructure.
Experience with GPU-based workloads and scalable compute environments.
Familiarity with security, data governance, auditability, and compliance requirements in healthcare or life sciences.
Experience working with clinical data platforms, healthcare AI, or regulated enterprise environments.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related technical discipline, or equivalent practical experience.
