AI Architect

Communication: Excellent verbal and written communication, with the ability to explain complex concepts simply and influence decisions. Architecture: Proven experience designing complex, distributed AI/ML systems and platforms end to end. AI/ML expertise: Strong grounding in machine learning, deep learning, and (ideally) GenAI/LLMs, including RAG and fine-tuning. MLOps: Hands-on experience with tools such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, Azure ML, or Databricks. Engineering: Strong programming skills (Python required), plus experience with Docker, Kubernetes, and microservices/API design. Cloud and data: Experience with AWS, Azure, or GCP, and with big data technologies (Spark, Kafka, data lakes/warehouses).Stakeholder management: A track record of working with senior stakeholders, managing expectations, and delivering across teams. Resilience: Comfortable with ambiguity, tight timelines, and challenging technical environments.