ML Engineer – AI Solutions

Job Description: ML Engineer – AI Solutions

We are seeking a highly skilled Machine Learning Engineer with 3-7 years of experience from a Tier-1 institute to join our team at Wadhwani Foundation. You will develop enterprise-grade, scalable ML models and robust pipelines while working under senior ML scientists and with software engineers. Your role involves building rigorously designed solutions that address societal challenges and serve as reliable decision-making tools for our partners. You'll be responsible for ensuring data integrity, integrating backend solutions, and optimizing AI workflows to create impactful, trustworthy systems that can be effectively deployed across the Foundation's diverse domains of interest.

Roles and Responsibilities:

• Have a strong research background and are adept at a variety of data mining/analysis methods and tools, building and implementing models, visualizing data, creating/using algorithms, and running simulations.
  
• Have enough education and experience to be able to quickly recognize a bad idea and lay out a process for designing and testing a potentially good one.
  

·       Should be able drive end-to-end AI development, from early PoC experimentation to production deployment. Responsibilities include building reproducible ML environments, developing ETL pipelines, setting up feature stores, implementing CI/CD for models, and ensuring continuous model monitoring and optimization. Ideal candidates should have experience with ML infrastructure, model serving, A/B testing, API integrations, and performance tuning for scalable AI solutions.

• Develop robust Machine Learning (ML) solutions, leveraging LLMs, Retrieval-Augmented Generation (RAG), and AI-driven analytics.
  
• Design and implement backend integrations for ML models, ensuring seamless deployment, API development, microservices architecture, and cloud-based scalability.
  
• Curate and preprocess structured and unstructured agricultural datasets, transforming them into ML-ready formats for training and validation.
  
• Contribute to algorithm development, execution, and scaled deployment, while defining metrics to evaluate model performance, efficiency, and real-world impact.
  
• Develop Fine-tuned / custom LLMs using frameworks like Hugging Face, LangChain, and LlamaIndex, optimizing RAG pipelines for domain-specific knowledge retrieval.
  
• Build and manage MLOps & LLMOps pipelines, ensuring automated model training, deployment, monitoring, and lifecycle management for scalable and reliable AI systems.
  
• Are comfortable working with cross-functional teams and have excellent communication skills and a track record of driving projects to completion.
  
• Have excellent communication skills and a willingness to adapt to the challenges of doing applied work for social good.
  
• Stay updated with cutting-edge ML, AI, MLOps, LLMOps and backend trends, integrating best practices to enhance reliability, efficiency, and scalability.
  

Desired Qualification : B.E. / B.Tech Computer Science / Electronics / Electrical Engineering from a Tier-1 Engineering institute.

Skillset :

• Machine Learning and Deep Learning:  Supervised, Semi-supervised, unsupervised, and reinforcement learning techniques, Feature engineering, model training, hyperparameter tuning, CNNs, LSTM, GRU, RNNs, Transformers and Attention Mechanisms, NLP, Computer Vision, and Time-Series Forecasting
  
• Programming:
  

                 Hands-on experience with Python libraries

• Popular neural network libraries
  
• Popular data science libraries (Pandas, numpy)
  

o   Knowledge of systems-level programming. Under the hood knowledge of C or C++

o   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

o   Database concepts; SQL, NoSQL

• AI Frameworks: LangChain, LlamaIndex, Hugging Face, TensorFlow/PyTorch
  
• Backend & Deployment: FastAPI, Flask, Docker, Kubernetes, CI/CD Pipelines
  
• Data Engineering: ETL/ELT, Data Lake, Lakehouse architecture, Vector Databases
  
• Cloud Platforms: AWS/GCP/Azure, API Gateway, Serverless Computing
  
• Data Annotation Tools: Labelbox, Scale AI, Prodigy, V7, Amazon SageMaker Ground Truth etc.
  
• LLMs & RAG – Prompt Engineering, LLM Fine-tuning, setting up RAG pipelines. Experience working with leading proprietary and open source LLMs for developing enterprise grade applications.
  
• Experience Designing and Executing Agentic AI Workflows for an enterprise or social sector use case would be an added plus.