Experience Required: 5 or more years of professional experience in machine learning engineering, AI systems development, or applied AI research Hands-on experience fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms Demonstrated experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments Experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns Strong written and verbal communication skills; ability to translate technical AI concepts for non-technical executive stakeholders Education Required: Bachelor's Degree Additional Information : ***HYBRID / 4 days per week in the office) 4. TensorFlow – 2–5 years building, training, and evaluating machine learning models using TensorFlow or TensorFlow Extended (TFX), including experience with model versioning, pipeline integration, and deploying models to production serving infrastructure.