Job Opportunity for Machine Learning Engineer at Wadhwani AI in Mumbai

Website Wadhwani AI

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.

Roles and responsibilities

An MLE will be responsible for building robust machine learning solutions to problems of societal importance; usually under the guidance of senior ML scientists, and in collaboration with dedicated software engineers. To our partners, a Wadhwani AI solution is generally a decision-making tool that requires some piece of data to engage. It will be your responsibility to ensure that the information provided using that piece of data is sound. This not only requires robust learned models, but pipelines over which those models can be built, tweaked, tested, and monitored. The following subsections provide details from the perspective of solution design:

Early-stage of proof of concept (PoC)

  • Setup and structure code bases that support an interactive ML experimentation process, as well as quick initial deployments
  • Develop and maintain toolsets and processes for ensuring the reproducibility of results
  • Code reviews with other technical team members at various stages of the PoC
  • Develop, extend, adopt a reliable, colab-like environment for ML

Late PoC

This is an early to mid-stage of AI product development

  • Develop ETL pipelines. These can also be shared and/or owned by data engineers
  • Setup and maintain feature stores, databases, and data catalogs. Ensuring data veracity and lineage of on-demand pulls
  • Develop and support model health metrics

Post PoC

Responsibilities during production deployment

  • Develop and support A/B testing. Set up continuous integration and development (CI/CD) processes and pipelines for models
  • Develop and support continuous model monitoring
  • Define and publish service-level agreements (SLAs) for model serving. Such agreements include model latency, throughput, and reliability
  • L1/L2/L3 support for model debugging
  • Develop and support model serving environments
  • Model compression and distillation

We realize this list is broad and extensive. While the ideal candidate has some exposure to each of these topics, we also envision great candidates being experts at some subset. If either of those cases happens to be you, please apply.


Candidates should have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.

Candidates should also possess a strong general knowledge of ML/AI concepts, and expert-level knowledge of how those concepts can be applied. Not being able to derive mathematical properties of AI concepts is fine. Not being able to quickly read and write code that implements an AI concept is not. A candidate should have enough years of education and experience that they can quickly recognize a bad idea, and layout a process for designing and testing a potentially good one.

Desired qualifications

Master’s degree or above in a STEM field. Several years of experience getting their hands dirty applying their craft.

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