Senior Machine Learning Operations Engineer

4 weeks ago

Bengaluru, Karnataka, India SatSure Full time
About SatSure

SatSure is a deep tech company that specializes in decision intelligence, primarily focusing on the nexus of agriculture, infrastructure, and climate action. Our mission is to make insights from earth observation data accessible to everyone, creating a significant impact on the developing world.

Job Summary

We are seeking a highly skilled Senior Machine Learning Operations Engineer to join our team. As a key member of our engineering team, you will be responsible for designing, building, and integrating large-scale, distributed machine learning systems. Your expertise in cutting-edge tools, distributed GPU training, and research experimentation will be crucial in enhancing our ML Ops capabilities.

Key Responsibilities
  • Architect and implement end-to-end life cycles of large-scale, distributed machine learning systems using cutting-edge tools and frameworks.
  • Develop tools and services for explainability of ML solutions, ensuring transparency and trust in our models.
  • Implement distributed cloud GPU training approaches for deep learning models, optimizing performance and scalability.
  • Build software and tools that improve the rate of experimentation for our research team, extracting valuable insights from data.
  • Identify and evaluate new patterns and technologies to enhance the performance, maintainability, and elegance of our machine learning systems.
  • Lead and execute technical projects to completion, collaborating with peers to build requirements and track progress.
  • Mentor fellow engineers in your areas of expertise, contributing 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
  • 5-8 years of professional experience in implementing MLOps frameworks to scale up ML in production.
  • Master's degree or PhD in Computer Science, Machine Learning/Deep Learning domains.
Must-Have Skills
  • 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 for 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.).
  • 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.
Interview Process
  • Intro call.
  • Assessment.
  • Presentation.
  • Interview rounds (ideally up to 3-4 rounds).
  • Culture Round/HR round.

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