MLOps Engineer

1 month ago

bangalore, India Clutterbot Full time

About Clutterbot
Imagine a world where you never have to worry about picking up clutter again At our robotics startup, we understand the challenges families face trying to balance work and daily responsibilities while keeping their homes tidy. That's why we're developing a cutting-edge household robot that will revolutionize the way you live. This safe and innovative robot will drive around your house, effortlessly picking up toys, clothes, and other items off the floor and organizing them neatly into containers. Say goodbye to clutter and hello to a more efficient and stress-free home life

As part of our close-knit team, you'll have the opportunity to work on cutting-edge technology and be at the forefront of the robotics industry. You'll also have the chance to grow your skills and expertise through collaboration with experienced professionals in the field. Join us in building the future of home automation and experience a fulfilling and rewarding career journey

About The Role
We seek a motivated MLOps Engineer to join our team and manage and automate our data pipeline for data collection, annotation, model training, dataset quality enhancement, annotation guideline improvement, and model management. You will play a critical role in ensuring that our ML models are robust, scalable, and efficient.

Responsibilities

-Automate data pipelines for collection, annotation, model training, dataset quality enhancement, and guideline improvement.
-Design and maintain ETL processes for seamless data flow to data warehouses and model training environments.
-Create dashboards to identify data issues (distribution shifts, duplicates, low-quality images) and showcase model performance with new datasets.
-Develop and deploy adaptive on-device ML models based on data from initial robot sessions.
-Collaborate with the ML team to identify key metrics and data sources for model fine-tuning.
-Implement algorithms that efficiently learn from new data and adapt to changing environments.
-Build data processing pipelines for feature extraction and data labeling for ML training.
-Automate the ML lifecycle: training, validation, deployment, monitoring, and retraining.
-Manage CI/CD pipelines for ML models, ensuring automated testing, validation, and deployment.
-Work with cross-functional teams to ensure successful ML model delivery.
-Stay updated with the latest in ML, data engineering, and MLOps, recommending improvements.
-Monitor and optimize data infrastructure performance and scalability, ensuring data integrity and security.
-Implement logging and monitoring for data pipelines and ML models to identify and resolve issues in real-time.
-Conduct regular code reviews, model evaluations, and performance audits to ensure best practices.
-Mentor junior team members and foster a collaborative team culture.
-Manage the ML model lifecycle from experimentation to production and ongoing maintenance, ensuring alignment with business objectives.

Background/Skills
-Bachelor's or Master's degree in Computer Science, Electrical Engineering, Mathematics, or a related field.
-3+ years of experience in machine learning engineering, focusing on developing and deploying ML models.
-Experience training and deploying ML computer vision models such as semantic segmentation, object detection, and image classification.
-Strong programming skills in Python or similar languages.
-Strong experience with popular ML frameworks such as TensorFlow or PyTorch.
-Strong experience with data processing and feature extraction techniques.
-Experience in working on ML model training and optimization for edge devices such as Nvidia Jetson.
-Familiarity with on-device learning and the challenges associated with it.
-Strong problem-solving and analytical skills, with the ability to analyze complex systems and identify areas for improvement.
- Excellent communication and collaboration skills, with the ability to work effectively in a team environment.

Designation: MLOps Engineer (ML + Data) - L2+

Benefits
● Team building activities
● Flexible work culture
● Company-Sponsored Devices.
● Fitness Benefits

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