Digital Python Digital Deep Learning Digital PySpark Generative AI
Digital Python Digital Deep Learning Digital PySpark Generative AI
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1 Vacancy
Job Description
Key Responsibilities:
- Data Pipeline Development: Building complete data pipelines from ingestion to transformation processing and visualization.
- Data Mining: Expertise in descriptive and predictive data mining using machine learning (ML) and natural language processing (NLP) techniques.
- AI Application Development: Developing modern AIbased applications using Large Language Models (LLMs) RetrievalAugmented Generation (RAG) and other cloud tools.
- Azure Expertise: Strong experience with Azure tools specifically in building AI and agentic applications.
- NLP Tasks: Performing sentiment analysis topic modeling Named Entity Recognition (NER) and text classification.
- Data Analysis & Insight Generation: Extracting insights and trends from crosssectional and timeseries data with a foundation in statistical methods.
- Collaboration & Communication: Effectively communicating insights and collaborating with crossfunctional teams.
- Leadership: Mentoring and guiding the team and facilitating scrum ceremonies.
Requirements
Required Technical Skills:
- Programming & Libraries: Python3 Pandas Numpy Pytorch TensorFlow SciPy Seaborn Dash PySpark Scikitlearn NLTK Spacy.
- Machine Learning: Knowledge of various ML models (Regression Decision Trees Random Forest SVM Ensemble methods Mixture of Experts).
- Data Exploration: Experience with exploratory data analysis (EDA) feature engineering and processing.
- NLP: Expertise in NLP tasks data wrangling embeddings Generative AI and prompt engineering.
- Methodologies: Agile Scrum.
- Data Access: SQL knowledge.
- Packaging & Deployment: Experience with ONNX Pickle Docker images.
- Web Development: Experience with FastAPI Streamlit GitOps.
Required Technical Skills: Programming & Libraries: Python3, Pandas, Numpy, Pytorch, TensorFlow, SciPy, Seaborn, Dash, PySpark, Scikit-learn, NLTK, Spacy. Machine Learning: Knowledge of various ML models (Regression, Decision Trees, Random Forest, SVM, Ensemble methods, Mixture of Experts). Data Exploration: Experience with exploratory data analysis (EDA), feature engineering, and processing. NLP: Expertise in NLP tasks, data wrangling, embeddings, Generative AI, and prompt engineering. Methodologies: Agile Scrum. Data Access: SQL knowledge. Packaging & Deployment: Experience with ONNX, Pickle, Docker images. Web Development: Experience with FastAPI, Streamlit, GitOps.
Employment Type
Full Time
