Platform Engineering Advisor

What you'll do

• Develop and implement machine learning, deep learning, and generative AI models using Python.
• 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 via 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 using FastAPI.
• Enforce enterprise security, compliance, and responsible AI governance across all GCP workloads.
• Prototype and integrate emerging GenAI capabilities including Gemini models and RAG pipelines.

What we're looking for

• Bachelor's degree or equivalent in computer science, engineering, information systems, or related field.
• Five to seven years of work experience in Platform Engineering or a related field.
• Experience providing leadership in planning, consulting settings, or as a senior member of multi-function teams.
• Proficiency in writing modular Python code for machine learning and generative AI models.
• Ability to build data pipelines using BigQuery, Cloud Dataflow, and Google Cloud Storage.
• Experience deploying containerized applications on Google Kubernetes Engine (GKE) or Cloud Run.
• Advanced knowledge of ML frameworks like PyTorch, TensorFlow, or Hugging Face (preferred).
• Proficiency in SQL and data libraries such as Pandas, Polars, or PyArrow (preferred).