Senior Machine Learning Operations Engineer
2 months ago
About SatSure
SatSure is a deep tech, decision Intelligence company that works primarily at the nexus of agriculture, infrastructure, and climate action creating an impact for the other millions, focusing on the developing world. We want to make insights from earth observation data accessible to all.
If you are interested in working in an environment that focuses on the impact on society, driven by cutting-edge technology, and where you will have the freedom to work on innovative ideas and be creative with no hierarchies, SatSure is the place for you.
Roles and 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 explainability of ML solutions.
- Implement distributed cloud GPU training approaches for deep learning models.
- Build software/tools that improves the rate of experimentation for the research team and extracting insights from it.
- Identify and evaluate new patterns and technologies to improve 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.
Requirements
Experience
- 5+ years of professional experience in implementing MLOps framework to scale up ML in production.
- 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.
Educational Qualifications:
- Bachelors, Masters, or PhD Degree in Computer Science/Machine Learning, SW Engineering.
Why Us?
- Opportunity to work on a unique and futuristic technology setup
- Flat organizational structure and accessibility
- Best in class leave policy
- Additional allowances for learning, skill development, broadband, medical insurance cover, etc.
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