Machine Learning Algorithms, Deep Learning, Image Processing, MongoDB, Predictive Analytics, Python, Visualization, Linux environments, Model monitoring, Advanced Statistical Modelling, NoSQL databases, Data Processing, Recommendation Systems, R, RDBMS SQL, Data Gathering, Large-scale datasets, Statistical Techniques

Tensorflow, System Design, Computer Vision, Python, Pyspark, Torch, Recommendation engines, Graph Knowledge Bases, Large-scale Search systems, Image Understanding models, Generative AI

data engineering , Machine Learning, Python, Sql, Data Analysis, Causal AI, Structured Causal Models

Sql, Machine Learning, Deep Learning, Data Science, Python, vector databases, RAG pipelines, applied AI, semantic data systems, enterprise knowledge grounding, semantic models, embeddings, enterprise ontologies, knowledge engineering, GenAI systems, big data infrastructure, semantic retrieval, Statistical Modeling

snowflake , Sql, Tensorflow, Pandas, Pytorch, Spark, Python, Scikit-learn, vector databases, MLflow, prompt engineering, Agentic AI systems, MLOps practices, MCP Model Context Protocol, LLM-powered applications, Kubeflow, Generative AI LLM Proficiency, RAG pipelines

Spark, ML Ops, Applied ML

Data Cleansing, data structures, test strategy development, propensity models, AI ML modeling, performance validation, recommendation engines

LangSmith (tracing, evaluations), Python, Pyspark, Sql, XGBoost, metrics, Scikit-Learn, LightGBM, Random Forest LangChain, LangGraph