Modern data platform tooling (e.g. Databricks or equivalent), Robotics telemetry, Data ingestion, AWS or comparable cloud infrastructure, Data pipelines, data models, Robotics data formats, ML models, Backend services

snowflake , Power Bi, Tableau, Sql, Tensorflow, Pandas, Pytorch, Numpy, Qlik, Databricks, Python, Airflow, scikit-learn, AWS Sagemaker, Nifi

Mqtt, Amqp, Databricks, AWS, Cloud Infrastructure, GenAI use cases, OPC-UA, ML models, Robotics telemetry, Field-equipment data streams, Retrieval-augmented generation workloads, Data pipelines, data models, Robotics data formats, Backend services

Matplotlib, Machine Learning, Hadoop, Power Bi, Tableau, Sql, Deep Learning, Tensorflow, MLops, Pytorch, Gcp, Spark, Seaborn, Azure, Python, AWS, Generative AI, LLMOps, Scikit-learn, reinforcement learning, Vector Databases, Agentic workflows, Prompt Engineering

model selection , Retrieval-Augmented Generation (RAG), Machine Learning, Natural Language Processing, Sql, Python, Data Ingestion, Feature Engineering, Data Science Libraries, Production Monitoring, Deep Learning Framework, Recommendation Systems, Time-Series Forecasting, Regularisation, Large-Scale Data Processing, Evaluation Metrics, MLOps Tooling, Personalisation Engines, Knowledge Graphs, Large Language Models

Machine Learning, SAS, Data Mining, Sql, Spark, Spss, Python, Data Science, Natural Language Processing, Predictive Modelling, Pattern Recognition, Deep Learning, pylearn2, Github Copilot, Claude Code, Vibe Coding, R, Databricks Genie, Business Intelligence, Expert Systems, Ai

Machine Learning, Hadoop, Sql, Deep Learning, Tensorflow, Data Quality, Pytorch, Gcp, MLops, Spark, Data Governance, Predictive Analytics, Azure, Python, AWS, Generative AI, LLMOps, Scikit-learn, reinforcement learning, Model Deployment

Algorithm Development, Machine Learning, Distributed Computing, MATLAB, Advanced Analytics, Python, Ai, Statistics, Data processing platforms, Mathematical Modelling, CI CD pipelines