Key Competencies Technical

5+ years of hands-on AI/ML engineering experience, including modern deep learning, LLMs, transformers, vector stores, retrieval techniques, and model evaluation.  Strong Python engineering skills and experience with core AI frameworks such as PyTorch, TensorFlow/Keras, and GenAI stacks like LangChain, LangGraph, LlamaIndex, and OpenAI/Azure/AWS SDKs.  Proven ability to evaluate build vs buy vs adapt decisions based on cost, effort, risk, and long-term maintainability.  Cloud-native AI development, preferably on AWS (SageMaker, Bedrock), with experience building scalable, secure, and compliant production systems.  Demonstrated experience with production-grade GenAI systems, including RAG pipelines, hybrid search, embedding strategies, and agentic workflows.  Strong MLOps background: CI/CD, model deployment, monitoring/observability, IaC, and workflow orchestration using tools like Prefect and Weights & Biases, or equivalents.  Experience with vector databases, scalable retrieval architectures, guardrailing LLMs and prompt engineering.  Familiarity with modern AI system design, including API serving, containerization (Docker), and distributed compute. Leadership & Collaboration  Experience working in Agile environments, including running or contributing to Scrum ceremonies, breaking down work into increments, and collaborating closely with TPM and EM in iterative delivery.  Ability to lead a team through ambiguity and early-stage environments.  Proactive style and strong ability for systematic problem solving.  Excellent communication capabilities across global departments.  Critical thinking in uncertain or rapidly evolving contexts.