AI Architect

Job Description:

Job Role :- AI Architect
Location:- Addison, TX (Day 1 Onsite)
Job Type: Full Time
Salary Range: $120000 to $140000/Annum + Full Time Benefits

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 LanEligible to workhain.
• Experience with GPU Eligible to workimization, 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 Consultantly.
• 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 Eligible to workimize 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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