Machine Learning Engineer
Location
Delhi | India
Job description
Summary
A Machine Learning Engineer (MLE) at Wadhwani AI will build rigorously designed and tested ML solutions capable of being deployed to a variety of audiences and domains that are of interest to the institute. High-quality models, and the pipelines that go into supporting those models, are the foundation on which our organization builds to have a positive societal impact.
Location - Delhi About Us Wadhwani AI is a nonprofit institute building and deploying applied AI solutions to solve critical issues in public health, agriculture, education, and urban development in underserved communities in the global south. We collaborate with governments, social sector organizations, academic and research institutions, and domain experts to identify real-world problems and develop practical AI solutions to tackle these issues to make a substantial positive impact.
We have over 30+ AI projects supported by leading philanthropies such as Bill & Melinda Gates Foundation, USAID and Google.org. With a team of over 200 professionals, our expertise encompasses AI/ML research and innovation, software engineering, domain knowledge, design and user research.
In the Press:
- Our Founder Donors are among the Top 100 AI Influencers
- G20 India's Presidency: AI Healthcare, Agriculture, & Education Solutions Showcased Globally.
- Unlocking the potentials of AI in Public Health
- Wadhwani AI Takes an Impact-First Approach to Applying Artificial Intelligence - data.org
- Winner of the H&M Foundation Global Change Award 2022
- Indian Winners of the 2019 Google AI Impact Challenge, and the first in the Asia Pacific to host Google Fellows
Roles And Responsibilities - Design and build robust, efficient, scalable ML pipelines to support data processing, model training, model inference, model evaluations, and deployment, while working across projects and verticals
- Define, execute, and enforce architectural vision to enable efficient experimentation and robust, reproducible, & rapid deployment
- Convert PoC ML models to scalable prototypes that can be deployed
- Define, develop, and drive risk mitigation strategies from ML point of view
- Define and drive SLAs for ML Systems including latency, throughput, memory, reliability, reproducibility, among others
- Architect overall ML platform including ETL pipelines, Data Stores, Feature Stores, Model stores, Model Training, Model Serving, Model Inference, and Post-Deployment Observability APIs
- Keep abreast with advances in ML by evaluating and experimenting with different ML frameworks and libraries to find the most appropriate tools for various tasks
- Manage ML platforms and associated infrastructure
- Ensure that every ML solution meets rigorous quality standards
- Mentor & coach team members
Minimum Qualifications - Undergraduate degree in Computer Science or equivalent and 6+ years in software development (at least 3 years in ML system development) OR Masters degree in Computer Science or equivalent and 4+ years in software development (at least 2 years in ML system development)
- A strong understanding of Machine Learning Life Cycle and ML Fundamentals
- Demonstrated experience in building teams and architecting engineering solutions
- Strong understanding of software development life cycle, ML and Software Engineering design patterns
- Proven experience with ecosystems such as Kubernetes; cloud providers such as AWS, GCP, Azure; and distributed computing
- Experience in modern Deep Learning frameworks such PyTorch, TensorFlow
- Strong programming and debugging skills. Especially in Python, Shell scripting.
DESIRED QUALIFICATION - Demonstrated experience in deploying edge solutions, including model compression and distillation techniques
- Experience in developing modular, plug-in, data-driven architectures, and ML Systems composed of many models and software components
- Full-stack software development experience is a plus
- Contributions to open source development with significant adoption by the community
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