Machine Learning Automation Engineer in San Jose

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Job DescriptionJob DescriptionDescriptionAbout the Role

We are seeking a Machine Learning Automation Engineer to join our Data and Analytics team. This role is ideal for someone passionate about building automation pipelines, experimenting with generative AI models, and optimizing ML workflows from data ingestion to deployment. You will collaborate with data scientists, data engineers, and automation architects to build scalable, intelligent systems that power next- analytics and agentic AI solutions.


Key Responsibilities

• Design and implement automated pipelines for data preprocessing, model training, evaluation, and deployment.
• Develop generative AI workflows (LLMs, diffusion models, transformers) for applied use cases such as synthetic data , RAG pipelines, and multimodal analysis.
• Build and maintain CI/CD for ML systems, ensuring reproducibility and model governance.
• Collaborate on feature engineering, hyperparameter tuning, and model validation using advanced statistical methods.
• Integrate MLOps frameworks (e.g., MLflow, Azure ML, AWS SageMaker) into automation processes.
• Prototype and evaluate new tools for AI-driven process automation and intelligent agent frameworks.
• Document experiments, track metrics, and support version control of datasets and models.

Required Skills & Qualifications

• Strong programming skills in Python (mandatory); familiarity with libraries such as NumPy, pandas, scikit-learn, PyTorch, TensorFlow, and LangChain.
• Working knowledge of generative AI (e.g., GPT, Llama, or similar LLMs).
• Solid understanding of statistics, probability, and data analysis (hypothesis testing, regression, A/B testing, Bayesian methods).
• Familiarity with automation frameworks (Airflow, Prefect, or similar) and version control (Git).
• Familiarity with AI powered code editors / IDE (eg., Cursor, Replit, VS Code, or similar)
• Working knowledge of API integration, prompt engineering, and vector databases (e.g., Pinecone, Qdrant, Weaviate).
• Understanding of data pipelines, model monitoring, and evaluation metrics.
• Excellent problem-solving skills and an eagerness to learn emerging AI technologies.

Qualifications/Course Work

• Coursework and project/prototype experience in Machine Learning, Deep Learning, or Generative AI.
• Exposure to MLOps and DevOps tools (Docker, Kubernetes, CI/CD).
• Familiarity with cloud platforms (Azure, AWS, or GCP).
• Familiarity with data visualization (Matplotlib, Power BI).
• Bachelor’s degree in mathematics, data science, computer science, engineering,  statistics or equivalent.

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