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 prioritize 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 modeling 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 is preferred to have. Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field 15+ years of experience in data science, machine learning, or advanced analytics 5+ years in a 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), plus 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 Google Cloud Platform) Experience driving enterprise AI transformation or center-of-excellence models Prior ownership of AI compliance, audit, or regulatory reviews