Sr AI Architect

5d•Hybrid

About The Position

We are looking for a Sr AI Architect to define and own the enterprise AI/ML vision, roadmap, and long-term strategy aligned with business goals. This role involves leading the design, development, deployment, and lifecycle management of AI and machine learning solutions. The architect will build and mentor high-performing AI, data science, and ML engineering teams, and partner with Product and Business leaders to identify high-impact AI use cases and prioritize initiatives. Key responsibilities include establishing best practices for model development, validation, monitoring, explainability, and retraining, ensuring AI solutions comply with data privacy, security, regulatory, and ethical AI guidelines, and overseeing AI platform architecture, model pipelines, and MLOps frameworks for scalability and reliability. The role also drives the adoption of generative AI, predictive analytics, NLP, and advanced modeling techniques, and communicates AI strategy, performance, and risks clearly to executive leadership and stakeholders. Additionally, the architect will evaluate and manage AI vendors, tools, platforms, and cloud services.

Requirements

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

Nice To Haves

Experience with Financial services, Wealth management is preferred 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 center-of-excellence models
Prior ownership of AI compliance, audit, or regulatory reviews

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