Track record of mentoring engineers and influencing technical direction across a team or organization Working knowledge of data privacy principles and practices (e.g., data minimization, access controls, privacy-preserving measurement) and experience applying them to ML data pipelines Experience implementing model governance frameworks, including approval workflows, audit trails, and compliance controls Experience implementing safety guardrails for LLM-powered systems, including content moderation, prompt-injection defenses, and red-teaming or adversarial evaluation practices Hands-on experience with observability and evaluation tools for LLMs (e.g., LangSmith, Weights & Biases, MLflow). Experience with distributed systems, cloud platforms (e.g., AWS), container orchestration (Kubernetes), CI/CD pipelines, and building Data Pipelines on Spark using Airflow Experience with ML lifecycle management and versioning practices, including experiment tracking, model registry, deployment automation, and dataset/model versioning tools (e.g., DVC, MLflow, Weights & Biases, Delta Lake) Experience with workflow orchestration platforms (Airflow) Excellent communication skills and a collaborative, team-oriented mindsetPh.