MLOps Engineer

1 month ago

Bengaluru, India Recro Full time

Job Title: Senior MLOps Engineer

Location: Bengaluru, Karnataka, India

Experience: 7-10 years


Responsibilities:


MLOps Framework and Infrastructure:

  • Create scalable MLOps frameworks and infrastructure to support the full machine learning lifecycle, from data ingestion to model deployment and monitoring.
  • Develop and maintain monitoring and alerting systems to track model performance, data drift, and system health, enabling proactive issue detection and resolution.
  • Work closely with data scientists and software engineers to seamlessly integrate machine learning models into production systems, prioritizing robustness, scalability, and performance.

CI/CD and Automation:

  • Implement automation and CI/CD practices to streamline model deployment, version control, and testing, ensuring efficient and reliable updates and rollbacks.
  • Responsible for implementing CI/CD for data engineering and machine learning platforms.

Architecting and Design:

  • Provide architecture and design for orchestrating the ML engineering and data engineering components/services.
  • Design and development of data engineering & ML engineering pipelines and production of them with optimum scaling.

Infrastructure and Provisioning:

  • Establish scalable and efficient infrastructure for training and inference, leveraging cloud platforms.
  • Responsible for setting up automated provisioning of GCP resources for machine learning and data engineering services.
  • Experience with infrastructure provisioning and configuring public and hybrid clouds with mandatory GCP experience.
  • Experience administering Kubernetes, Google Kubernetes Engine (GKE), and understanding of manifest management with Helm.

Data Engineering and ML Operations:

  • Responsible for building automated pipelines using Airflow/Kubeflow for running data engineering and machine learning components.
  • Responsible for building scalable solutions to bring AI models to production.
  • Adopt best practices for writing processed data (ML features) to appropriate data lakes, warehouses, or feature stores.

DevOps Management:

  • Manage code repositories and innovate development and release procedures.
  • Configure automated deployment on multiple environments.
  • Manage JIRA stories related to DevOps work.
  • Setup of monitoring and alerts for production workloads in Google Cloud Platform.

Automation and Optimization:

  • Recommend and implement automated solutions to improve the performance and reliability of the system.
  • Experience in automating multiple systems using Bash and languages such as Python and Go.

Collaboration and Communication:

  • Strong communication skills across the board, with a passion for finding and sharing best practices and driving greater discipline.
  • Excellent verbal and written communication skills in English.


Basic Requirements:


Experience:

  • 8+ years of IT experience, with at least 4+ years of relevant experience in DevOps, CloudOps, Dockerization, and Containerization.
  • Experience with CI/CD pipelines and related tools such as Jenkins, CircleCI, or Google Cloud Build.
  • Experience with configuration management tools and deployment tools like Terraform and Google Deployment Manager.
  • Experience with Dockerization of machine learning scripts and deployment in Google Cloud Platform.
  • Experience with iteration on running products, experiment management, and data versioning.
  • Experience with Google Cloud Platform, Kubernetes, Google Kubernetes Engine (GKE), and Helm.
  • Experience with Agile and Scrum development processes.

Technical Skills:

  • Strong understanding of MLOps practices, including Git, CI/CD, input data unit and statistical testing, experiment tracking, model registry, scheduling of ML pipelines, production drift monitoring and alerting, and cloud computing optimization.
  • Strong understanding of distributed computing, data warehousing, and big data technologies.
  • Strong programming skills in Bash, Python, and Go.
  • Strong understanding of data engineering and ML engineering.

Soft Skills:

  • Excellent problem-solving and analytical skills.
  • Strong communication and collaboration skills.
  • Passion for finding and sharing best practices and driving greater discipline.


Nice to Have:

Linux/Unix:

  • Experience in managing Linux/Unix platforms and application server administration (e.g., Tomcat, JBoss).
  • Experience with DNS, Linux system configuration, and administration (CentOS, RedHat).

Monitoring Tools:

  • Experience with monitoring tools geared towards user experience and deep diagnostics (e.g., Splunk, New Relic, AppDynamics, Prometheus, Grafana).

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