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