Sql, Tensorflow, MLops, Gcp, Pytorch, Docker, Spark, Azure, Python, Kubernetes, AWS, Observability and Monitoring, Scikit-learn, API and Microservices Architecture, Vector Databases, AI Evaluation Frameworks, Feature Stores, Reinforcement Learning concepts, Model Deployment, Agentic AI Systems, Experiment Tracking

Github, Sql, Git, MLops, Docker, Databricks, Kubernetes, Python, Orchestration, Testing, Prefect, R, GitHub Actions, CI/CD pipelines, GitOps, Production Monitoring, Software engineering best practices, Model versioning, Containerisation, Unity Catalog, Lineage

cloud platform , Kafka, Data Modeling, Data Governance, Data Warehouse, ELT, Nosql, Python, AWS, BigQuery, Apache Spark, Data Warehousing, Sql, Gcp, Amazon Redshift, Data Lake, Databricks, Azure, Etl, Medallion Architecture, Trino, Data access management, Data marketplace, Big Data Architecture, Distributed data processing, Data Lakehouse, rbac, Data lake technologies

Ml, Deep Learning, Tensorflow, Nlp, Pytorch, Gcp, Python, AWS, LLMs, SLMs, auto-classification, anomaly detection

Tensorflow, Machine Learning, Pytorch, Opencv, Python, Ocr, Deep Learning, GenAI, LLMs, PIL

Pyspark, Sql, Tensorflow, Pytorch, XGBoost, Python, real-time inference latency-sensitive model serving, LightGBM, Scikit-learn, model monitoring in production environments, incrementality measurement, Auction systems and ads marketplaces, Representation learning embeddings, Bandits and exploration strategies, online experimentation, Multi-objective optimization, Learning-to-Rank

Tensorflow, Pytorch, Gcp, Azure, Python, Kubernetes, AWS, MLops, Scikit-learn

Python, Sql, Tensorflow, MLops, AWS, Pytorch, Kubernetes, Azure, Gcp, Docker, Spark, API and Microservices Architecture, AI Evaluation Frameworks, Observability and Monitoring, Feature Stores, Scikit-learn, Agentic AI Systems, Reinforcement Learning concepts, Vector Databases, Experiment Tracking, Model Deployment

MLops, Spark, Databricks, Python, Kubernetes, AWS, rollback, deployment strategies, experiment tracking, ML infrastructure, versioning, embedding retrieval systems, classical ML models, Ray, LLM agent workflows, reproducible training data, model validation, model registry, Production Monitoring, NLP models

Distributed Computing, Apache Airflow, Cloud Infrastructure, Linux, Terraform, metrics, System Design, Distributed Systems, Kubernetes, Python, Logging, Infrastructure as Code, Alerting, MLflow, Reliability, Monitoring