Platform Engineering Advisor

What you'll do

• Write modular Python code to develop and implement machine learning, deep learning, and generative AI models.
• Build scalable data engineering and preprocessing workflows using BigQuery, Cloud Dataflow, and Cloud Storage.
• Design and maintain automated ML pipelines for training, hyperparameter tuning, and deployment using Vertex AI Pipelines.
• Package and deploy production models using Vertex AI Endpoints, Cloud Run, or Google Kubernetes Engine.
• Implement MLOps observability to monitor for data drift, anomalous inputs, and latency regressions.
• Transition experimental Python prototypes into production-grade microservices and expose them via REST/gRPC APIs.
• Enforce enterprise security and responsible AI governance using IAM best practices and VPC Service Controls.
• Prototype emerging GenAI capabilities including Gemini models and fine-tuning techniques within the GCP ecosystem.

What we're looking for

• Bachelor's Degree or equivalent in computer science, engineering, or information systems.
• 5 to work experience in Platform Engineering or a related field.
• Experience providing leadership in a general planning or consulting setting and as a senior member of multi-function project teams.
• Proficiency in writing clean, modular Python code to develop machine learning, deep learning, and generative AI models.
• Experience building scalable data engineering workflows using BigQuery, Cloud Dataflow (Apache Beam), and Cloud Storage (GCS).
• Experience designing and maintaining automated ML pipelines using Vertex AI Pipelines (Kubeflow/TFX) and deploying models to GKE or Cloud Run.
• Mastery of modern Python (3.10+), object-oriented programming, and API development using FastAPI (preferred).
• Deep expertise in ML frameworks like PyTorch, TensorFlow, or JAX and experience with LLM orchestration like LangChain (preferred).