Platform Engineering Advisor

What you'll do

• Develop and implement machine learning, deep learning, and generative AI models using Python.
• Build scalable data engineering, feature transformation, and preprocessing workflows using BigQuery and Cloud Dataflow.
• Design and maintain automated ML pipelines for training, hyperparameter tuning, and deployment using Vertex AI Pipelines.
• Deploy containerized models to production environments such as Cloud Run or Google Kubernetes Engine.
• Implement MLOps observability to monitor model performance, data drift, and system latency.
• Convert experimental research prototypes into robust, production-grade microservices via REST/gRPC APIs.
• Enforce enterprise security, compliance, and responsible AI governance across all cloud workloads.
• Prototype emerging GenAI capabilities including Gemini models and RAG pipelines within the GCP ecosystem.

What we're looking for

• Bachelor's degree or equivalent formal training in computer science, engineering, or information systems.
• Five to seven years of work experience in Platform Engineering or a related field.
• Mastery of modern Python including asynchronous programming, API development with FastAPI/Flask, and testing with pytest.
• Expertise in machine learning frameworks such as PyTorch, TensorFlow, JAX, or Hugging Face Transformers.
• Experience building LLM-powered applications using Vertex AI, LangChain, LangGraph, or LlamaIndex.
• Proficiency in SQL, BigQuery, and data libraries like Pandas, Polars, and PyArrow.
• Experience with MLOps tools including Vertex AI Pipelines, Docker, Kubernetes (GKE), and Terraform for infrastructure as code.
• Ability to develop ETL/ELT pipelines using Cloud Dataflow, Dataproc, or Cloud Composer.