Python with Machine Learning (GenAI)

Title: Python with Machine Learning (GenAI) Location: PLEASANTON, CA Duration: 6 monthsMust Have Technical/Functional Skills Hands-on experience on: Programming Languages Strong Python familiarity (hands-on) for data prep, modeling, and building ML components. SQL - Skills: joins, window functions, CTEs, query optimization Machine Learning Linear/Logistic Regression Decision Trees, Random Forest, XGBoost, LightGBM SVM, KNN Model evaluation - Precision/Recall, F1, ROC-AUC, MSE, RMSE Model tuning - Grid search, randomized search, cross-validation Deep Learning Frameworks: TensorFlow, Keras, PyTorch CNNs, RNNs, LSTMs, Transformers Use cases: NLP, computer vision, time-series forecasting Data Wrangling & Preprocessing Missing data handling Feature engineering Data cleaning Outlier detection Normalization/standardization 5. Data Visualization & BI Tools Python: Matplotlib, Seaborn, Plotly Tools: Tableau, Power BI Dashboards, reporting, storytelling with data 6. Big Data & Cloud Tools (Needed for production-scale roles) Big Data Frameworks: Spark, Hadoop Cloud Platforms (any one strongly): o AWS (S3, EC2, SageMaker) o Azure (Data Factory, Databricks, ML Studio) o GCP (BigQuery, Vertex AI) 7. Deployment Skills (advanced roles) Model deployment: Flask, FastAPI Docker, Kubernetes (optional) CI/CD basics 8. Databases & Data Engineering Basics Relational: MySQL, PostgreSQL, SQL Server NoSQL: MongoDB, Cassandra Data pipelines: Airflow, Prefect (optional)Roles & Responsibilities Define the ML use case, success metrics, and evaluation criteria; Liaise with business directly and translate business needs into an ML approach. Perform data exploration, data quality checks, feature engineering, and dataset preparation for training and testing. Build, train, validate, and iterate ML models; compare experiments and select the best candidate model. Package the solution for production (e.g., containerized scoring/service endpoint) and support deployment with engineering/MLOps practices Set up basic monitoring (model accuracy/health) and support continuous improvement post-release. Required Skills & Experience Solid foundation in ML concepts (supervised/unsupervised, evaluation, validation) and practical experimentation. Experience taking models to production in a cloud-agnostic way (portable design; API/service mindset). Working knowledge of version control and basic CI/CD-style collaboration with engineering teams. Role Descriptions: AI/AM| GenAI Essential Skills: AI/AM| GenAI Desirable Skills: Azure data factory| Databricks| Azure Synapse Analytics|Python| Sql |Pyspark Databricks Keyword: :Azure data factory| Databricks| Azure Synapse Analytics|Python| Sql |Pyspark Databricks: Skills: Digital : Artificial Intelligence(AI):Generative AI Experience Required: 10 & Above