Machine Learning Engineer Pleasanton - CA - California

Hands-on experience on:

1. Programming Languages

Strong Python familiarity (hands-on) for data prep, modeling, and building ML components.

SQL - Skills: joins, window functions, CTEs, query optimization

2. Machine Learning

LinearLogistic Regression

Decision Trees, Random Forest, XGBoost, LightGBM

SVM, KNN

Model evaluation - PrecisionRecall, F1, ROC-AUC, MSE, RMSE

Model tuning - Grid search, randomized search, cross-validation

3. Deep Learning

Frameworks: TensorFlow, Keras, PyTorch

CNNs, RNNs, LSTMs, Transformers

Use cases: NLP, computer vision, time-series forecasting

4. Data Wrangling & Preprocessing

Missing data handling

Feature engineering

Data cleaning

Outlier detection

Normalizationstandardization 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 Google Cloud Platform (BigQuery, Vertex AI) 7. Deployment Skills (advanced roles) Model deployment: Flask, FastAPI Docker, Kubernetes (optional) CICD 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 scoringservice endpoint) and support

deployment with engineeringMLOps practices

Set up basic monitoring (model accuracyhealth) and support continuous improvement

post-release. Required Skills & Experience

Solid foundation in ML concepts (supervisedunsupervised, evaluation, validation) and practical

experimentation.

Experience taking models to production in a cloud-agnostic way (portable design APIservice

mindset).

Working knowledge of version control and basic CICD-style collaboration with engineering

teams.