Java, Scala, Data Structures, JIRA, Devops, Algorithms, MLops, Data Scientist, Data Engineer, Python, Generative AI, LLMOps, MLE, LLM integration, vector databases, embedding models, Agile tools

Tensorflow, Pytorch, MLops, Opencv, Python, LLMOps, Model Fine-tuning, Model Optimization, mlflow, kubeflow, Deep Learning Techniques, NLP Technologies, Classic ML Techniques and Frameworks

Apache Spark, Sql, Tensorflow, Pytorch, Databricks, Python, Machine Learning Algorithms, model versioning, deep learning frameworks, Lakehouse architectures, cloud platforms, MLflow, Monitoring, MLOps practices, Statistical Modeling, drift detection, CI CD for ML, automated retraining

Pytorch, MLops, Python, Kubernetes, Hugging Face, vLLM Triton, Vector Databases, LLM Gateway, Agentic AI, Self-hosted Models, RAG, LLM Generative AI, LLM Orchestration

Machine Learning, C++, Perl, Python, Computer Vision, Loans, Operations Research

Sql, Python, time-series forecasting, Generative AI tooling, R, tree-based models, classical ML modeling, observability monitoring tools

AWS, Sql, Grafana, Machine Learning, MLops, Prometheus, Artificial Intelligence, Kubernetes, Python, Azure, Docker, Large Language Models, Prompt Engineering, LLMOps, Data Bricks, Agentic AI, Generative AI

containerization , Java, Microservices, Automated Testing, Kubernetes, Python, Apis, Databases, stream-processing technologies, orchestration technologies, observability tooling, Go, cloud platforms, generative AI tools, data engineering pipelines

Ml, Summarization, Nlp, Pytorch, Python, fine-tuning, versioning, structured clinical abstraction, Documentation, reproducibility, weak supervision, entity extraction, patient-trial matching, eligibility reasoning, Llm, tests, Active Learning, prompting, peer review, Normalization, RAG, hybrid ML

ML frameworks (e.g., MLAS), Java, C, C++, Pytorch, Python, Parallel execution strategies, SIMD vectorization, ONNX, Performance profiling, Cache-aware execution, XNNPACK, Benchmarking, Optimization, qemu