Sr. AI Architect

Key Responsibilities Define and own the enterprise AI/ML vision, roadmap, and long-term strategy aligned with business goals Lead design, development, deployment and lifecycle management of AI and machine learning solutions Build and mentor high‑performing AI, data science and ML engineering teams Partner with Product and Business leaders to identify high‑impact AI use cases and prioritise initiatives Establish best practices for model development, validation, monitoring, explainability and retraining Ensure AI solutions comply with data privacy, security, regulatory and ethical AI guidelines Oversee AI platform architecture, model pipelines and MLOps frameworks for scalability and reliability Drive adoption of generative AI, predictive analytics, NLP and advanced modelling techniques where applicable Communicate AI strategy, performance and risks clearly to executive leadership and stakeholders Evaluate and manage AI vendors, tools, platforms and cloud services Required Qualifications Experience with Financial services, Wealth management preferred Bachelor's or Master's degree in Computer Science, Data Science, Engineering or a related field 15+ years of experience in data science, machine learning or advanced analytics 5+ years in an architecture or people‑management role overseeing AI/ML teams Strong hands‑on experience with ML models, statistical methods and AI frameworks Experience deploying AI solutions in production environments at scale Hands‑on RAG experience: embeddings, retrieval strategies, chunking, metadata/routing, evals and guardrails Open source frameworks: strong with LangChain (or similar) and experience with FastAPI/Flask and async patterns Azure: practical experience with Azure OpenAI/Models, Azure AI Search, Azure ML, AKS/Container Apps, Key Vault, App Insights/Log Analytics and hybrid private networking Terraform: modules, CI/CD integration and handling nested data structures MLOps/DevOps: Docker, Kubernetes, CI/CD (Gitlab or Azure DevOps), secrets management and automated testing Solid understanding of LLMs (prompting, function/tool calling, structured outputs, rate limiting, token/cost management) Proficiency with Python, SQL and modern data/ML platforms (cloud‑based preferred) Strong understanding of data governance, model risk management and responsible AI practices Excellent communication skills with the ability to translate complex AI concepts for non‑technical audiences Preferred / Nice‑to‑Have Experience in financial services, healthcare, life sciences or other regulated industries Exposure to generative AI, large language models (LLMs) and prompt engineering Familiarity with MLOps tools, CI/CD for ML and cloud platforms (AWS, Azure or GCP) Experience driving enterprise AI transformation or centre‑of‑excellence models Prior ownership of AI compliance, audit or regulatory reviews #J-18808-Ljbffr W3Global