Highly Skilled AI Infrastructure Specialist
4 days ago
Razorpay is seeking an experienced Senior MLOps Engineer to drive the scalability and reliability of our machine learning infrastructure.
This role requires collaboration with Data Scientists, Machine Learning Engineers, and other stakeholders to streamline model deployment and maintenance, ensuring robust production-grade solutions.
Key Responsibilities:- Collaborate Effectively: Partner with Data Scientists and ML Engineers to transform model requirements into efficient production solutions.
- Enhance ML Infrastructure: Contribute to key projects including feature store, model-serving platform, and model registry to elevate Razorpay's machine learning capabilities.
- Optimize ML Pipelines: Oversee and refine machine learning pipelines focusing on training, evaluation, and deployment. Enhance processes to boost cost efficiency and reduce runtime using platforms like DataRobot.
- Improve Real-time Reliability: Design and implement strategies to reduce latency and increase reliability for real-time feature and model serving.
- Implement Best Practices: Establish and promote version control standards, CI/CD processes, and automated testing for robust ML model deployment.
- Manage Cloud Infrastructure: Oversee cloud resources, manage ML-related data storage and compute instances, and drive improvements across the infrastructure stack.
- Stay Current with Emerging Technologies: Continuously explore and integrate new tools and advancements in the MLOps ecosystem to refine and improve our systems.
- Document and Share Knowledge: Maintain comprehensive documentation of processes, architectures, and workflows to foster knowledge sharing and team cohesion.
- Build & Manage Cloud Environments: Experience managing cloud environments, with a strong preference for AWS. Proficiency in infrastructure automation tools like Terraform and configuration management tools like Helm, Puppet, Chef, or Ansible.
- Proficient in CI/CD & Version Control: Skilled with CI/CD tools and version control systems (e.g., Git) to maintain reliable deployment processes.
- Containerization & Orchestration: Hands-on experience with Docker, Kubernetes, and other containerization technologies.
- ML Frameworks & Tools: Knowledgeable in machine learning concepts and frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps platforms like MLflow, Kubeflow, or DataRobot.
- Distributed Systems Experience: Ability to build and maintain low-latency, distributed backend APIs.
- Problem-solving & Collaboration: Strong analytical skills and the ability to work collaboratively in a team-oriented environment.
The estimated annual salary for this position is ₹25,00,000 - ₹35,00,000 based on location and industry standards.
LocationBangalore
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