Description

Key Responsibilities

  • Design and implement scalable MLOps platforms and machine learning deployment pipelines.
  • Build and maintain CI/CD pipelines for machine learning models and applications.
  • Automate model training, validation, deployment, monitoring, and retraining workflows.
  • Deploy machine learning models to cloud and on-premises production environments.
  • Develop automated ML pipelines using tools such as MLflow, Kubeflow, Airflow, or equivalent platforms.
  • Containerize ML applications and services using Docker.
  • Deploy and manage ML workloads using Kubernetes and container orchestration platforms.
  • Implement model versioning, experiment tracking, model registry, and artifact management.
  • Monitor model performance, data quality, system health, latency, availability, and resource utilization.
  • Implement automated model retraining and continuous machine learning workflows.
  • Develop infrastructure using Infrastructure as Code tools such as Terraform or CloudFormation.
  • Integrate ML platforms with AWS, Microsoft Azure, or Google Cloud Platform.
  • Build APIs and model-serving infrastructure using REST APIs, FastAPI, or similar technologies.
  • Collaborate with Data Scientists to productionize machine learning and deep learning models.
  • Implement security, access control, secrets management, and compliance for ML environments.
  • Troubleshoot production ML pipelines, infrastructure, deployment, and performance issues.
  • Optimize cloud infrastructure and compute resources for cost, scalability, and performance.
  • Establish observability and monitoring using tools such as Prometheus, Grafana, CloudWatch, or Azure Monitor.
  • Implement automated testing for ML pipelines, data workflows, and model deployments.
  • Maintain technical documentation, architecture diagrams, deployment procedures, and operational runbooks.

Technical Skills

Programming & Data

  • Python
  • SQL
  • Bash/Shell scripting
  • Pandas
  • NumPy

Machine Learning & MLOps

  • MLflow
  • Kubeflow
  • TensorFlow
  • PyTorch
  • Scikit-learn
  • Model Registry
  • Model Versioning
  • Feature Engineering
  • Model Monitoring
  • Model Serving

Cloud Platforms

  • AWS
  • Microsoft Azure
  • Google Cloud Platform (GCP)
  • Amazon SageMaker
  • Azure Machine Learning
  • Google Vertex AI

DevOps & Infrastructure

  • Docker
  • Kubernetes
  • Helm
  • Terraform
  • Ansible
  • Git
  • GitHub/GitLab
  • Jenkins
  • GitHub Actions
  • Azure DevOps

Data & Workflow Platforms

  • Apache Airflow
  • Apache Spark
  • Databricks
  • Snowflake
  • Kafka

Monitoring & Observability

  • Prometheus
  • Grafana
  • ELK Stack
  • CloudWatch
  • Azure Monitor
  • Application Insights

API & Model Deployment

  • REST APIs
  • FastAPI
  • Flask
  • Model Serving
  • Microservices
  • API Gateways