Machine Learning Operations Engineer (ML Ops - 2)

We are looking for a Machine Learning Operations Engineer to join our team, to design, build, and integrate ML Ops for large-scale, distributed machine learning systems, focusing on cutting-edge tools, distributed GPU training, and enhancing research experimentation.

Roles & Responsibilities:

• Architect, build, and integrate end-to-end life cycles of large-scale, distributed machine learning systems i.e., ML Ops, using cutting-edge tools/frameworks.
• Develop tools and services for the explainability of ML solutions.
• Implement distributed cloud GPU training approaches for deep learning models.
• Build software/tools that improve the rate of experimentation for the research team and extract insights from it.
• Identify and evaluate new patterns and technologies to improve the performance, maintainability, and elegance of our machine learning systems.
• Lead and execute technical projects to completion. Communicate with peers to build requirements and track progress.
• Mentor fellow engineers in your areas of expertise - Contribute to a team culture that values effective collaboration, technical excellence, and innovation.
• Collaborate with engineers across various functions to solve complex data problems at scale.

Qualification:

• 5 - 8 years of professional experience in implementing the MLOps framework to scale up ML in production.
• Master’s degree or PhD in Computer Science, Machine Learning / Deep Learning domains

Must-have:

• Hands-on experience with Kubernetes, Kubeflow, MLflow, Sagemaker, and other ML model experiment management tools, including training, inference, and evaluation.
• Experience in ML model serving (TorchServe, TensorFlow Serving, NVIDIA Triton inference server, etc.)
• Proficiency with ML model training frameworks (PyTorch, PyTorch Lightning, Tensorflow, etc.).
• Experience with GPU computing to do data and model training parallelism.
• Solid software engineering skills in developing systems for production.
• Strong expertise in Python.
• Building end-to-end data systems as an ML Engineer, Platform Engineer, or equivalent.
• Experience working with cloud data processing technologies (S3, ECR, Lambda, AWS, Spark, Dask, ElasticSearch, Presto, SQL, etc.).
• Having Geospatial / Remote sensing experience is a plus.
• Competencies:
• Excellent debugging and critical thinking skills.
• Excellent analytical and problem-solving skills.
• Ability to work in a fast-paced, team-based environment.

Benefits:

• Medical Health Cover for you and your family, including unlimited online doctor consultations
• Access to mental health experts for you and your family
• Dedicated allowances for learning and skill development
• Comprehensive leave policy with casual leaves, paid leaves, marriage leaves, bereavement leaves
• Twice a year appraisal

Job Type: Full-time

Work Location: In person