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

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

Machine Learning, Kafka, Sql, MLops, Docker, Spark, Python, Kubernetes, AWS, Ai, anomaly detection, Real-time data processing, Time-series forecasting

error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models

Natural Language Processing, Deep Learning, Python, Scala, Machine Learning, Search Technologies, Retrieval Augmented Generation, Large Language Models, reinforcement learning, Go, Embeddings

Golang, Jax, Prometheus, Cloudwatch, Grafana, Tensorflow, Pytorch, Python, Java, Scala, Spark, Keras, Kubernetes, MLflow

model development , Data Cleaning, Sql, Numpy, Pandas, Python, offline evaluation, time-series data anomaly detection, scikit-learn, field validation, Forecasting, Classification, hybrid ML plus heuristics, Regression, rule-based systems, deployment monitoring, signal validation, drift detection, feature engineering

Tensorflow, Machine Learning, Pytorch, MLops, Natural Language Processing, Python, Sql, Deep Learning, Vector Databases, Generative AI, Retrieval-Augmented Generation

snowflake , Sql, Emr, Pandas, Machine Learning, BigQuery, Aws S3, Numpy, Python, Redshift, LLM systems, Snorkel, Airflow, WhyLabs, Label Studio, OpenSearch, scikit-learn, dbt, data-quality controls, ICD-10-CM, Glue, pgvector, Arize, Cpt, Evidently, data pipelines, Hcpcs

data engineering , model selection , Numpy, Pandas, Pytorch, Python, scikit-learn, ML pipelines, Hypothesis formulation and testing, ELT data pipelines, Cross-validation, ML research methodologies, Performance benchmarking, Reproducible research, Production ML systems