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

Sklearn, Pandas, Power Bi, SAS, Qlik, Tableau, Keras, Python, Sql, Tensorflow, R

Mqtt, Amqp, Databricks, AWS, data pipelines, GenAI use cases, retrieval-augmented generation, robotics data formats, Data Collection, analytics layer, large language model, OPC-UA, ML models, data models, backend services, robotics telemetry

Data Quality, Data Governance, Sql, Python, analytics best practices, machine learning libraries, data visualisations

software engineering practices , data preparation , model development , Machine Learning, MLops, Python, Sql, Feature Engineering, Ai

Machine Learning, SAS, Data Mining, Sql, Spark, Spss, Python, pylearn2, Github Copilot, Claude Code, Vibe Coding, R, Databricks Genie, Business Intelligence

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

Tensorflow, MLops, Pytorch, Python, Sql, Deep Learning, Vector Databases, LLMOps, reinforcement learning, Agentic Workflows

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