Employer description & source details
Must have experience to design, build and run GenAI and Agentic AI solutions inside enterprise systems, fitting AI into existing systems, security rules and budgets. Must have ability to translate ambiguous business problems into durable technical architectures. Must have strong ML fundamentals; how models are trained, tested and compared; spotting overfitting and weak data, deep learning basics; embeddings, classification, ranking models Should be able to architect and implement scalable ML, Generative AI, and Agentic AI solutions (LLMs, RAG, autonomous agents) aligned with business objectives. Must have experience in data science, ML engineering or AI architecture, with at least 5 years leading ML solutions from design to production. Should have expertise in Python, SQL and Solid statistics, including outlier detection and error metrics Should have exposure to time series forecasting. Should have production experience with regression and tabular ML: feature engineering, model selection, calibration, uncertainty (conformal or quantile intervals) and explainability (SHAP, coefficients). Must have experience with hierarchical forecasting or reconciliation, and prompt-injection testing. Should have hands-on experience with embeddings, vector search and hybrid retrieval, and with LLM applications: tool calling, retrieval-grounded generation, citations, guardrails and evaluation. Should have hands-on experience for building conversational agent, with multi-turn session memory and tool calling into the multiple engines and the semantic layer. Must have experience on a major cloud: pipelines, model registry, drift monitoring, retraining and low-latency serving. Should have experience with Google Cloud (Vertex AI, BigQuery, Gemini, Vector Search), or equivalent on another clou
Extracted skills: python, sql, security