Jitendra Kumar Sharma

About Jitendra Kumar Sharma

Results-driven Data Science and AI/ML professional with expertise across the full data…

Experience

1. Ai Ml Consultant

   Twitter

   Oct 2022 — Present

   Proven experience in AI/ML solution design, model development, and deployment. Knowlwdge of large-scale distributed systems (Kafka, Spark, Snowflake).Conduct pioneering research in Generative AI, LLMs, LMMs, and reinforcement learning.Build scalable ML pipelines for large-scale training data lifecycle – acquisition, processing, curation, annotation, versioning, and quality control.Proficiency in Python and ML frameworks (TensorFlow, PyTorch, JAX, Scikit-learn, Hugging Face Transformers).Knowledge of MLOps tools (Kubeflow, MLflow, TFX) and data versioning.Hands-on experience with distributed systems and training large-scale ML models.Build and optimize end-to-end ML pipelines for data ingestion, preprocessing, training, and deployment.Deploy and manage ML workloads on Google Cloud (Vertex AI, Cloud Storage, Compute Engine, Pub/Sub, Cloud Functions, BigQuery).Familiarity with MLOps practices (data versioning with DVC, experiment tracking, CI/CD).Contributions to open-source projects, patents, or academic publications in AI/data management.Expertise in data quality assessment, governance, annotation, and labeling techniques.Experience collaborating with product teams for real-world AI/ML technology transfer.Research or industry experience in training generative AI models (pre-training or post-training) in one or more modalities (image, video, 3D, audio).Expertise in large-scale model training & optimization (distributed training, data curation, memory-efficient techniques).Experience with post-training techniques (fine-tuning, alignment, distillation).Proven ability to build, deploy, and maintain large-scale generative models (GANs, diffusion models, Transformers).Experience in designing and scaling distributed ML pipelines for generative AI.Strong understanding of end-to-end ML data lifecycle for large-scale generative models.Hands-on experience with cloud-based data platforms (AWS, GCP, Azure, Spark, Databricks, Snowflake).