ROLES AND RESPONSIBILITIES

An ML Engineer at Wadhwani AI 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 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. Setup 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.

DESIRED QUALIFICATIONS

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

Programming

• Expert level Python programmer
• Hands-on experience with Python libraries
  • Popular neural network libraries
  • Popular data science libraries (Pandas, numpy)
• Knowledge of systems-level programming. Under the hood knowledge of C or C++
• Experience and knowledge of various tools that fit into the model building pipeline. There are several – you should be able to speak to the pluses and minuses of a variety of tools given some challenge within the ML development pipeline
• Database concepts; SQL
• Experience with cloud platforms is a plus