ML Ops Engineer
ML Ops Engineer
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Job Description
About YUBI
Yubi formerly known as CredAvenue is redefining global debt markets by freeing the flow of finance between borrowers lenders and investors. We are the worlds 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.
In 2020 we began our journey with a vision of transforming and deepening the global institutional debt market through technology. Our twosided debt marketplace helps institutional and HNI investors find the widest network of corporate borrowers and debt products on one side and helps corporates to discover investors and access debt capital efficiently on the other side. Switching between platforms is easy which means investors can lend invest and trade bonds all in one place. All 5 of our platforms shake up the traditional debt ecosystem and offer new ways of digital finance.
Yubi Loans Term loans and working capital solutions for enterprises.
Yubi Invest Bond issuance and investments for institutional and retail participants.
Yubi Pool Endtoend securitisations and portfolio buyouts.
Yubi Flow A supply chain platform that offers trade financing solutions.
Yubi For banks and NBFCs for colending partnerships.
Currently we have boarded over 4000 corporates 350 investors and have facilitated debt volumes of over INR 40000 crore.
Backed by marquee investors like Insight Partners B Capital Group Dragoneer Sequoia Capital LightSpeed and Lightrock we are the onlyofitskind 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 likeminded 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.
Responsibilities:
Develop and enhance the ML platform to standardize model development and deployment workflows.
Create reusable components to streamline the Data Science teams efforts and expedite the model lifecycle.
Integrate models seamlessly with various products and systems.
Implement robust logging and instrumentation for monitoring scoring requests for models in production.
Establish systems for continuous model monitoring and trigger mechanisms for retraining based on performance metrics.
Design and build A/B testing frameworks with support for canary deployments and shadow models to evaluate different model versions.
Integrate data pipelines necessary for model retraining and update activities in production.
Scale training and inference capabilities using standardized environment setups and deployment strategies.
Incorporate opensource frameworks and proprietary tools into the MLOps pipeline to achieve development goals.
Prototype and evaluate different opensource frameworks to identify optimal technology stacks for the pipeline.
Focus on CI/CD pipeline integration for models and ensure seamless deployments in various environments.
Requirements
Experience & Expertise :
3 years of experience in MLOps software development and deploying machine learning models.
Strong programming skills in Python with experience in scripting languages for automation.
Must have extensive experience with Docker and Kubernetes including the ability to create manage and debug containerized environments as well as deploy and monitor Data Science solutions in production.
Excellent problemsolving abilities and analytical mindset.
Experience with deploying and monitoring Data Science solutions in production environments.
Handson experience with MLOps tools and frameworks such as MLFlow Seldon KubeFlow and cloud services like AWS Sagemaker Azure ML or GCP AI Platform.
Indepth understanding of public cloud infrastructure and services (AWS Azure GCP).
Proven track record of evaluating and building prototypes with opensource tools to guide technology choices.
Responsibilities: Develop and enhance the ML platform to standardize model development and deployment workflows. Create reusable components to streamline the Data Science team's efforts and expedite the model lifecycle. Integrate models seamlessly with various products and systems. Implement robust logging and instrumentation for monitoring scoring requests for models in production. Establish systems for continuous model monitoring and trigger mechanisms for retraining based on performance metrics. Design and build A/B testing frameworks with support for canary deployments and shadow models to evaluate different model versions. Integrate data pipelines necessary for model retraining and update activities in production. Scale training and inference capabilities using standardized environment setups and deployment strategies. Incorporate open-source frameworks and proprietary tools into the MLOps pipeline to achieve development goals. Prototype and evaluate different open-source frameworks to identify optimal technology stacks for the pipeline. Focus on CI/CD pipeline integration for models and ensure seamless deployments in various environments. Experience & Expertise : 3+ years of experience in MLOps, software development, and deploying machine learning models. Strong programming skills in Python, with experience in scripting languages for automation. Must have extensive experience with Docker and Kubernetes, including the ability to create, manage, and debug containerized environments, as well as deploy and monitor Data Science solutions in production. Excellent problem-solving abilities and analytical mindset. Experience with deploying and monitoring Data Science solutions in production environments. Hands-on experience with MLOps tools and frameworks such as MLFlow, Seldon, KubeFlow, and cloud services like AWS Sagemaker, Azure ML, or GCP AI Platform. In-depth understanding of public cloud infrastructure and services (AWS, Azure, GCP). Proven track record of evaluating and building prototypes with open-source tools to guide technology choices.
Employment Type
Full Time
