Upendra Singh — AI Researcher

I don’t just build models—I deploy them, scale them, monitor them, and tune them until they hum. With years of deep, hands-on experience in machine learning, data engineering, and MLOps, I specialize in turning messy data into production-grade intelligence. Whether it's training deep learning systems, optimizing pipelines, or experimenting with the latest in generative AI, I'm all in. I believe in being hands-on always! I thrive in fast-paced, problem-solving environments and love collaborating with teams that care about both smart algorithms and clean code. From notebooks to Kubernetes, I bridge the gap between research and robust, real-world applications. As a Senior Principal Engineer(Machine Learning), I have close to 16+ years of hands on experience and expertise in: • Understanding business objectives and developing models/techniques that can help to achieve them, along with metrics to track their progress • Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability • Exploring and Visualising data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world • Defining data/model validation strategies • Defining the pre-processing or feature engineering to be done on a given dataset(s) and performing required data engineering to achieve the same or work closely with data engineering teams to get it done • Analysing the errors of the model and designing strategies to overcome them • Deploying/Monitoring models in production • Integrating implemented ML Systems with existing application(s) or platforms • Designing Machine Learning Systems to support Batch and Real Time data processing platforms • Researching and implementing appropriate ML algorithms and tools • Performing relevant data engineering(big data if need be!) for collecting, ingesting and transforming data for ML Experiments and analysis Key Technical Skills: Various Problem Spaces Worked Upon: Generative AI(Agentic), Reinforcement Learning, Web Mining, Knowledge Graph(Creation and Augmentation), Probabilistic Entity/Identity Resolution, Unified Customer Profile, Time Series Forecasting, Fraud Detection, Anomaly Detection. Experience with Various ML/DL Techniques: Machine Learning(Classification, Regression), Clustering, Network Analysis, Deep Learning(Tensorflow, Pytorch using Keras API), NLP using Deep Learning, Named Entity Recognition, Relationship Extraction, Bayesian Analysis, LangChain, LangGraph

Stackforce AI infers this person is a SaaS-focused Machine Learning Architect with expertise in data-driven solutions.

Location: Bangalore Urban, Karnataka, India

Experience: 18 yrs 6 mos