AI Architect-Data science/GenAI

Role: AI Architect

Must Have Technical/Functional Skills

Primary Skill: Data science, architecture, genAI architecture

Secondary: Python, communication

Experience: 10+ years

Roles & Responsibilities

Required Experience & Technical Skills

· 8+ years of software/solution architecture experience, including 3+ years in AI/ML or Generative AI.

· Hands-on experience with LLMs, embeddings, and vector search technologies (OpenAI, Hugging Face, Llama, Elasticsearch).

· Strong Python development skills, including FastAPI and LangChain.

· Experience with GPU optimization, cloud-native deployments, and MLOps practices.

· Excellent communication and presentation skills, including presenting to executive leadership.

Education & Professional Attributes

· Master’s degree in Computer Science, Data Science, or a related technical discipline.

· Experience collaborating with diverse business and technology stakeholders across multiple Lines of Business.

· Highly motivated self-starter with strong ownership and ability to execute independently.

· Strong critical thinking and problem-solving capabilities.

· Ability to navigate enterprise data assets across multiple functions.

· Highly organized with the ability to manage multiple priorities in a fast-paced environment.

· Strong analytical and customer-focused mindset.

Key Responsibilities

· Design and scale enterprise-grade Document AI platforms for the financial services industry.

· Lead architecture for document classification, data extraction, and Retrieval-Augmented Generation (RAG) solutions.

· Architect GenAI solutions for large-scale document processing, extraction, and question-answering.

· Build and optimize RAG pipelines using embeddings, vector databases, rerankers, and LLMs.

· Partner with infrastructure teams to deploy AI solutions on GPU clusters using technologies such as vLLM and Triton.

· Drive model risk management, explainability, auditability, and evaluation frameworks.

· Create architecture diagrams, technical documentation, and executive-level presentations.

· Collaborate closely with engineering, product, and compliance teams to deliver secure, scalable AI solutions.