Architect Agentic Systems: Design and lead the implementation of complex, multi-agent AI workflows capable of advanced reasoning, planning, and autonomous execution using frameworks like LangGraph, CrewAI, and Google ADK.
Solution Design & Development: Translate complex business problems within the risk domain into well-defined technical requirements, and develop robust, end-to-end AI solutions to address them.
Performance & Cost Management: Architect AI solutions with a focus on cost-efficiency by implementing strategies for token optimization, model selection, and caching to ensure financial sustainability.
LLM Orchestration: Build and optimize retrieval pipelines, memory layers, and tool-use sequences using frameworks like LangChain.
Backend & API Engineering: Develop robust, scalable Python-based microservices and REST APIs using FastAPI to expose AI capabilities.
RAG Implementation: Construct and refine Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, embedding, and vector search integration with databases like Azure AI Search or Pinecone.
Containerization & Deployment: Package AI services using Docker and deploy them on Kubernetes, contributing to CI/CD pipelines for smooth and reliable releases.
Observability & Evaluation: Instrument AI workflows using platforms like Langfuse for tracing and debugging. Implement and maintain evaluation harnesses to ensure model quality and performance.

10+ years of professional experience in a role blending software development and data science/machine learning.
Expert-level Python development skills.
Proven track record of designing and building scalable backend services and APIs (FastAPI preferred).
Deep, hands-on experience designing and building solutions with multiple agentic frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).
Extensive experience architecting and optimizing RAG systems and integrating with vector databases (e.g., OpenSearch, Pinecone, Weaviate).
Proven expertise in designing and deploying containerized (Docker/Kubernetes) AI systems on a major cloud platform (AWS, Azure, or GCP).
Strong experience implementing MLOps principles, including CI/CD, observability, and evaluation frameworks for LLM-based systems.
Proven ability to design and implement cost-effective AI architectures, with a deep understanding of tokenomics, model-tiering strategies, and caching for performance and budget management.
In-depth understanding of AI risk, safety, and enterprise governance requirements.
Strong background in ML, deep learning, and NLP, including Transformer architectures.
Bachelor's degree in Computer Science, Engineering, Business, or a related field.

Vice President, Risk Modeling Solutions – GEN AI

Key skills