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ML Engineer
Yubi
Chennai•On-site•3-6 Yrs•Jun 27
Job description
Overview
Yubi stands for ubiquitous. But Yubi will also stand for transparency, collaboration, and the power of possibility.
Responsibilities
- Yubi, formerly known as CredAvenue, is re-defining global debt markets by freeing the flow of finance between borrowers, lenders, and investors. We are the world's possibility platform for the discovery, investment, fulfilment, and collection of any debt solution. At Yubi, opportunities are plenty and we equip you with tools to seize it.
- In March 2022, we became India’s fastest fintech and most impactful startup to join the unicorn club with a Series B fundraising round of $137 million.
- Yubi Loans – Term loans and working capital solutions for enterprises.
- Yubi Invest – Bond issuance and investments for institutional and retail participants.
- Yubi Pool– End-to-end securitisations and portfolio buyouts.
- Yubi Flow – A supply chain platform that offers trade financing solutions.
- Yubi Co.Lend – For banks and NBFCs for co-lending partnerships.
- Currently, we have boarded over 4000+ corporates, 350+ investors and have facilitated debt volumes of over INR 40,000 crore.
- Backed by marquee investors like Insight Partners, B Capital Group, Dragoneer, Sequoia Capital, LightSpeed and Lightrock, we are the only-of-its-kind debt platform globally, revolutionizing the segment.
- At Yubi, People are at the core of the business and our most valuable assets. Yubi is constantly growing, with 650+ like-minded individuals today, who are changing the way people perceive debt. We are a fun bunch who are highly motivated and driven to create a purposeful impact. Come, join the club to be a part of our epic growth story.
- MLOps Engineer
- Develop and maintain the core ML platform to standardize model development and deployment workflows.
- Create and evangelize reusable MLOps components to accelerate the Data Science model lifecycle.
- Design and implement seamless integration of trained models with diverse production systems and products.
- Implement robust logging and instrumentation for real-time monitoring of model scoring requests in production.
- Establish automated systems for continuous model monitoring and performance-based retraining.
- Design and build advanced deployment strategies, including Blue-Green Deployment and A/B testing frameworks that support canary and shadow models.
- Optimize model inference at scale: Implement request-based scaling (e.g., KEDA), ensure eventual distribution of requests to pods, and explore removing node affinity for flexible load distribution.
- Enhance CI/CD and deployment efficiency:
- Reduce container startup time (e.g., Docker layer optimization).
- Integrate unit test case flow directly into the deployment pipeline.
- Enable auto deployment from GitHub for seamless, GitOps-style operations.
- Scale and manage compute resources:
- Implement multi-resource configuration for optimal pod provisioning.
- Troubleshoot and fix issues related to KEDA-based scaling in QA and Production environments.
- Explore advanced GPU architectures (e.g., NVIDIA MIG) and implement fractional GPU usage for cost and resource optimization.
- Automate Model and Infrastructure Configuration:
- Implement automatic model loading from MLflow using cloud storage (EFS/S3) for efficient serving.
- Investigate and implement Agentic auto-configuration of pods to intelligently adapt resources based on traffic/load.
- Integrate and optimize data pipelines necessary for scheduled model retraining and production updates.
Qualifications
- 3+ years of professional experience in MLOps, software engineering, and successfully deploying production-ready machine learning models.
- Strong expertise in Python and advanced scripting for pipeline automation and tooling development.
- Extensive hands-on experience with Docker and Kubernetes for building, managing, and debugging containerized ML environments and scalable production deployments.
- Deep proficiency with MLOps frameworks (e.g., MLflow, Seldon, Kubeflow) and production-level cloud ML services (e.g., AWS SageMaker, Azure ML, GCP AI Platform).
- In-depth understanding of public cloud infrastructure and services (AWS, Azure, or GCP).
- Proven ability to evaluate, prototype, and implement open-source tools to define and guide the MLOps technology stack.
- Excellent problem-solving skills and a strong analytical mindset.