AI Technical Architect

About the Role

We are looking for an experienced AI Technical Architect who is responsible for designing enterprise AI solutions that align with business objectives while ensuring scalability, security, and efficiency. This role translates business needs into AI-driven architectures (across data ingestion, models and operational workflows), ensuring feasibility, cost-effectiveness, and adherence to industry best practices and firmwide standards. They provide deep experience in designing viable, feasible, performant AI solutions.

Responsibilities

• Design enterprise AI solutions that align with business objectives while ensuring scalability, security, and efficiency
• Translate business needs into AI-driven architectures across data ingestion, models, and operational workflows
• Ensure feasibility, cost-effectiveness, and adherence to industry best practices and firmwide standards
• Build and deploy AI solutions primarily on Microsoft Azure (Azure ML, Azure OpenAI)
• Ensure adherence to cloud architecture best practices, scalability, and reliability standards

Qualifications

• 10+ years of overall experience including at least 2 years in Architect role
• Proven experience architecting AI-driven solutions for enterprise customers, collaborating with R&D and engineering teams
• Expertise in AI Architecture, including LLM/SLM deployment, fine-tuning, inference optimization, retrieval-augmented generation (RAG), API-based AI deployment and model orchestration
• Proficiency in Cloud Security & FinOps, including AI landing zones, model refinement and testing, and compliance strategies
• Experience in Enterprise AI Design, focusing on scalable, secure, and cost-effective AI integration
• Understanding of data science principles, enabling informed evaluation of machine learning and statistical models

Skills

• Complete life cycle AI/ML experience
• Deep ML/LLM systems engineering
• RAG and Vector Stores
• Agent Workflow Building
• Hands-on delivery focused
• Multi-Agent Systems
• Strong experience building real GenAI solutions including RAG implementations
• LangChain, LangGraph, Semantic Kernel, AutoGen or Llamaindex
• Prompt engineering
• Vector databases & embeddings
• Comfortable leading delivery and making technical decisions
• Able to code, prototype, and guide teams by example