Lesson 2/6: Solution. Not Product.

Find out why, at Wadhwani AI, we don’t stop asking whether an AI-based solution is possible and whether it can potentially make a big difference, and learn more about the roles of Programs and AI Research in our innovation process.
At Wadhwani AI, we don’t stop with asking whether an AI-based solution is possible and whether it can potentially make a big difference.

On Amazon, when you buy rechargeable batteries, it is unlikely that it recommends a battery charger that is not in stock or can’t be delivered in a reasonable time. That’s because, even as Amazon’s AI teams build great recommender systems, its engineers, designers, and product managers figure out how the AI works with the rest of their systems in a way that customer satisfaction (and revenue, in the long term) is maximized.

Building AI for social good needs the same mindset.

(See the first post in this series – Lesson 1: User-centered? What about the beneficiaries, the choosers, and the payers?)

Let’s take the example of Wadhwani AI’s work related to pest management in cotton.* The AI we are building identifies and counts pests from photos of pest traps taken by lead farmers. AI’s output is used to provide timely advice on what pesticides to use, how much, and when. Problem solved? No!

For this AI to make a difference, a few things have to happen both upstream and downstream from the app with AI. For example: Upstream, the pest traps must be set up and monitored; Downstream, the advice generated, say, spraying, or not spraying, pesticides, must be acted upon. Easier said than done.

In the case of Amazon, economic alignment of all stakeholders ensures a certain level of coordination and drives adoption of smoothly-functioning digital infrastructure (hardware, software, connectivity). It’s a virtuous cycle that, over time, makes it easy for product managers and designers to focus on smaller pieces of the large puzzle, with confidence that they can tackle/influence other pieces if required in service of the big picture.

In contrast, stakeholders’ interests in large scale programs — say, government health or agriculture programs — do not pull in the same direction. The pesticide supplier, a local monopoly of sorts, may encourage more pesticide usage, not less. The apps deployed for use by field staff are generally designed for ‘reporting’ purposes, not for making data useful for farmers. And, generally speaking, prescribed protocols and actual practice are rarely the same.

So, in social development settings, taking a solution perspective can seem daunting. It is tempting for technology innovators to focus on great AI or product, and hope that other systemic issues will get resolved in due course. But the ‘technology-for-good’ experience of the past decade serves as a warning. Government and NGO programs are groaning under the weight of additional work created by software in silos and semi-digitized workflows. Med-tech and ag-tech innovators bemoan programs’ inability to adopt their products for impact. Administrators lament innovators’ inability to see the big picture.

That is why, at Wadhwani AI, we don’t stop with asking whether an AI-based solution is possible and whether it can potentially make a big difference. We also look for partners — NGOs and governments — to co-create, iterate, pilot, and scale the said solution.

This focus on solution, as opposed to AI or product, is reflected in our innovation process. Our product managers write a ‘Solution Requirement Document’** not your typical PRD, i.e., product requirement document!

* Wadhwani AI was one of the winners of the global Google AI Impact Challenge for this work, but it’s still early days and we expect plenty of changes in the coming months as we learn from our field experiments.

** Solution Requirements Document (SRD) is a living document that evolves as we learn. Product managers write it in collaboration with programs, design, engineering and AI research, and with inputs from key partners externally.

P.S.: Scroll down for a side note on AI Research and Programs — two functions most product managers, designers, and engineers would not have worked closely with.

PSST! We are hiring. Write to us if you are a world-beating product manager or design researcher: careers@wadhwaniai.org. Don’t forget to mention this blog!

Side note on Programs and AI Research functions

It may be worth explaining the roles of Programs and AI Research in our innovation process, because they are two functions typically not seen in most product teams. 

Programs is like external operations and business development functions rolled into one. They are closest to customers and users in the social development world. They take the lead in engaging governments and NGO programs, accessing/collecting data, finding relevant domain expertise (say, entomologists or epidemiologists), running field experiments, and so on. They help answer questions like: What are the key systemic constraints to keep in mind? Are workflow modifications possible? Can we collect this data? Who should we partner with? What is the path to scale?

AI Research team, besides building AI at the core of a solution, help rest of the team understand what’s possible (or not) and what new research directions are worth pursuing. They keep abreast of the latest research, which is usually ahead of industry practice. They help rest of the team understand: What data are required? How might current workflows need to change? How should we evaluate new models before scaling?

Coming Up:

We will talk about internal stakeholders (AI researchers, engineers, program managers, and leadership) in subsequent posts. Sign up here to receive updates about new articles in this series.

Lesson 3: Data, data everywhere. Not a drop to drink.

Lesson 4: AI research is iterative. Very iterative.

Lesson 5: UI for AI. AI for UI.

Lesson 6: Agile with clay feet

  • Wadhwani AI

    We are an independent and nonprofit institute developing multiple AI-based solutions in healthcare and agriculture, to bring about sustainable social impact at scale through the use of artificial intelligence.


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.