Machine Learning Operations Engineer

3 weeks ago

Mumbai, Maharashtra, India Accelya Full time
Job Summary

The Machine Learning Operations Engineer will be responsible for designing, developing, and deploying machine learning models and algorithms to solve complex problems and improve our products and services. The ideal candidate will have a solid foundation in machine learning principles, hands-on experience with various ML frameworks, and a strong ability to translate business requirements into scalable ML solutions.

Key Responsibilities
  1. Model Deployment and Integration:
    - Deploy machine learning models into production environments and ensure seamless integration with existing systems.
    - Collaborate with software engineers to automate and streamline the deployment pipeline for ML models.
  2. Model Monitoring and Maintenance:
    - Continuously monitor the performance of machine learning models in production to ensure they are functioning correctly.
    - Set up alerts for performance degradation, model drift, and other anomalies.
    - Regularly update models with new data, retraining, and redeployment as necessary.
  3. Automation and CI/CD Pipelines:
    - Build, optimize, and maintain continuous integration/continuous deployment (CI/CD) pipelines for ML workflows.
    - Automate processes such as model versioning, testing, and deployment.
  4. Infrastructure Management:
    - Design, configure, and maintain the infrastructure needed for scalable ML model training and inference (cloud services, on-premise hardware, Kubernetes, Docker, etc.).
    - Ensure the infrastructure can handle large datasets, compute-intensive tasks, and high availability.
  5. Collaboration with Data Science and Engineering Teams:
    - Work closely with data scientists to understand the model's requirements and improve model performance.
    - Ensure the reproducibility of models across different environments by supporting containerization and version control.
  6. Data Pipeline Management:
    - Design and maintain data pipelines for continuous flow of training and testing data into the machine learning lifecycle.
    - Ensure data quality, security, and privacy compliance (e.g., GDPR, HIPAA).
  7. Version Control and Model Governance:
    - Implement model versioning and ensure traceability of models, datasets, and training experiments.
    - Work within frameworks for model governance, ensuring transparency, auditing, and compliance for deployed models.
  8. Performance Optimization:
    - Optimize the performance of machine learning models by tuning hyperparameters, adjusting infrastructure, and resolving performance bottlenecks.
    - Ensure efficient resource usage for both model training and inference (e.g., cost optimization for cloud infrastructure).
  9. Security and Compliance:
    - Ensure security of ML models, training data, and inference data.
    - Implement measures to prevent adversarial attacks and ensure the models meet necessary regulatory standards.
Requirements and Qualifications
  • Technical Skills:
    - Machine Learning & Deep Learning: Solid understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn) and deployment challenges.
    - Cloud Platforms: Proficiency with cloud services (AWS, Azure, GCP) for deploying and managing ML workloads.
    - Containerization and Orchestration: Experience with Docker, Kubernetes, and related technologies for containerizing and orchestrating ML workflows.
    - DevOps Practices: Knowledge of CI/CD tools like Jenkins, GitLab CI, or CircleCI for automating ML pipelines.
    - Model Management Tools: Familiarity with MLflow, DVC (Data Version Control), or similar tools for managing models and experiments.
    - Data Engineering: Strong experience in designing and optimizing data pipelines (using tools like Apache Kafka, Apache Spark, or Airflow).
    - Version Control: Proficient in Git for managing code and ML model versioning.
    - Programming Languages: Expertise in Python and other languages used in ML and familiarity with scripting languages like Bash.
  • Soft Skills:
    - Problem-Solving: Strong troubleshooting and problem-solving abilities, particularly in the context of productionizing machine learning models.
    - Collaboration: Ability to work effectively with cross-functional teams (data science, software engineering, IT).
    - Communication: Strong written and verbal communication skills to articulate technical concepts to non-technical stakeholders.
    - Project Management: Ability to prioritize tasks, manage timelines, and balance multiple responsibilities in an agile environment.
  • Understanding of Best Practices:
    - Model Lifecycle Management: Knowledge of the full ML model lifecycle from development, testing, deployment, to monitoring.
    - Security Best Practices: Awareness of security considerations when deploying models (e.g., data encryption, access control).
    - Scalability and Performance Tuning: Expertise in optimizing models and infrastructure for scalability, reliability, and cost-efficiency.
    - Compliance and Ethical Considerations: Knowledge of privacy laws, ethical AI guidelines, and best practices for AI governance.
  • Additional Knowledge (Desirable):
    - Automation/Infrastructure as Code (IaC): Familiarity with tools like Terraform or Ansible for automating infrastructure setup.
    - Serverless Computing: Understanding of serverless architectures for ML workloads (e.g., AWS Lambda, Google Cloud Functions).
    - Data Privacy & Security Regulations: Experience with GDPR, HIPAA, and other relevant data protection regulations.
    - AI/ML Governance: Familiarity with AI/ML governance tools to track model performance, fairness, and interpretability.
Estimated Salary Range

$120,000 - $180,000 per year.

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