TB-LPA and an urgent need for a solution

India carries the world’s largest burden of drug-resistant TB.

Numbers in medicine hold great importance. Especially when it comes to TB. India carries the world’s largest burden of drug-resistant TB. Global TB reports suggest that there are 124,000 MDR-TB cases annually. Before you move forward, it is important to understand how India handles this TB pandemic. This post is good to get the conversation started.

These numbers have been bothering lawmakers and healthcare professionals across the world. The problem can get worse if the patient isn’t diagnosed on time and starts transmitting it to others. When India’s war against TB started, it would take up to three months to accurately diagnose a patient. In 2008, the healthcare world got a reprieve when Line Probe Assay (LPA) was introduced to diagnose drug resistance among TB patients. This is a rapid molecular diagnostic technique which brought down the turnaround time from three months to just about five days.

LPA is a series of genetic markers laid out in a binary form.

How does LPA work? It is a series of genetic markers laid out in a binary form. These genetic markers act like a barcode on a product you buy in the supermarket. The barcode identifies the product, its manufacturer, the date it was manufactured on, the batch number and when it expires. This genetic marker identifies the kind of TB the patient may have and what they could be sensitive to.

NTEP mandates that every confirmed pulmonary TB patient should be tested for drug sensitivity. This means that approximately 1.2 million newly diagnosed pulmonary TB patients should be tested for drug sensitivity testing. Currently, there are 61 culture and drug sensitivity test (CDST) laboratories that conduct the LPA test. And they manage to cover about 400,000 patients every year. There are plans to expand capabilities to service 1.2 million patients in the next two years. 

Remember the importance of numbers? Here’s where it comes in. Once the genetic markers are recorded on that sheet above, it is interpreted to a form and then digitized and entered into a national database. All of this is a manual process, which involves several man-hours spent by lab technicians, microbiologists and data entry operators. Even if you consider just those who have been diagnosed with MDR-TB, the number baffles the mind. 

And in manual processes, there is always scope for improving efficiency and avoiding potential errors. Any errors can cause great harm.

How can AI help?

We imagine a solution that can read these genetic markers, increasing efficiency and accuracy, and decreasing time for diagnosis. The time savings will also enable lab technicians as well as microbiologists to focus on more important tasks. Not only can the AI read the LPA but it can also populate the NIKSHAY database without too much trouble.

AI can be an elegant solution to this problem. But some solutions are best found in partnership. And that’s why we are seeking partners to come forward and help us forge a new path. 

ML Engineer


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.


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


  • 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

ML Scientist


As an ML Scientist at Wadhwani AI, you will be responsible for building robust machine learning solutions to problems of societal importance, usually under the guidance of senior ML scientists. You will participate in translating a problem in the social sector to a well-defined AI problem, in the development and execution of algorithms and solutions to the problem, in the successful and scaled deployment of the AI solution, and in defining appropriate metrics to evaluate the effectiveness of the deployed solution.

In order to apply machine learning for social good, you will need to understand user challenges and their context, curate and transform data, train and validate models, run simulations, and broadly derive insights from data. In doing so, you will work in cross-functional teams spanning ML modeling, engineering, product, and domain experts. You will also interface with social sector organizations as appropriate.  


Associate ML scientists will have a strong academic background in a quantitative field (see below) at the Bachelor’s or Master’s level, with project experience in applied machine learning. They will possess demonstrable skills in coding, data mining and analysis, and building and implementing ML or statistical models. Where needed, they will have to learn and adapt to the requirements imposed by real-life, scaled deployments. 

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


  • B.Tech./B.E./B.S./M.Tech./M.E./M.S./M.Sc. or equivalent in Computer Science, Electrical Engineering, Statistics, Applied Mathematics, Physics, Economics, or a relevant quantitative field. Work experience beyond the terminal degree will determine the appropriate seniority level.
  • Solid software engineering skills across one or multiple languages including Python, C++, Java.
  • Interest in applying software engineering practices to ML projects.
  • Track record of project work in applied machine learning. Experience in applying AI models to concrete real-world problems is a plus.
  • Strong verbal and written communication skills in English.