Senior Data Scientist / ML Engineer

Job Description: Senior Data Scientist / ML Engineer

Only W2 candidate ready to go F2F

• Location: Diamond Bar, CA (Onsite)


• Work Mode: Onsite (Full-Time Contract)


• Experience Range: 4 - 6 Years


• Payroll: Celebal Technologies (Contract Track)

Position Overview

We are seeking a highly skilled Senior Data Scientist / ML Engineer to design, build, and deploy scalable AI and machine learning solutions within the Azure cloud environment. The ideal candidate will leverage deep technical expertise in the Azure Databricks ecosystem to deliver both traditional ML pipelines and cutting-edge AI applications—including Computer Vision and Generative AI—driving business impact through data-driven innovation.

Core Technical Skills

• Azure Databricks Stack: Hands-on experience with Databricks Workspace, PySpark, MLflow, Delta Lake, and integration with Azure cloud data services (e.g., Azure Data Lake, Synapse Analytics).


• Core Languages & Tools: Strong proficiency in Python (including libraries like Pandas, Scikit-learn, NumPy), SQL, statistical modeling, and data visualization (e.g., Matplotlib, Seaborn, Power BI).


• Machine Learning & MLOps: Proven experience in building, training, evaluating, and deploying ML models—along with end-to-end MLOps workflows using MLflow, CI/CD, and containerization (e.g., Docker, Kubernetes).


• Computer Vision: Experience implementing vision-based AI solutions, such as object detection, image classification, or segmentation using frameworks like YOLO, OpenCV, or PyTorch/TensorFlow.


• Generative AI (Preferred): Familiarity with LLMs, RAG architectures, prompt engineering, and Azure OpenAI Service is a strong plus.

Key Responsibilities

• Design, develop, and deploy scalable end-to-end ML pipelines (data ingestion → preprocessing → modeling → deployment → monitoring) using Azure Databricks and MLflow.


• Conduct advanced data engineering tasks—including data cleaning, transformation, and feature engineering—using PySpark and SQL on large-scale datasets.


• Collaborate with cross-functional teams (Product, Engineering, Business Stakeholders) to translate business requirements into robust AI/ML solutions.


• Stay current with emerging trends and tools in AI/ML, especially in Generative AI and MLOps, and recommend best practices for adoption.


• Ensure model performance, reliability, and compliance with organizational governance and security standards.

Benefits & Perks

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