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
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
Never miss an opportunity! Create an alert based on the job you applied for.