K&K Global Talent Solutions Inc. is an international recruiting agency that has been providing technical resources in the Canada and the USA region since 1993.
This position is with one of our clients in USA, who is actively hiring candidates to expand their teams.
Role:- Data science/AI Architect (10+ Year)
Location:- Addison, TX (Onsite)
Fulltime
Job Description
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.
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