What are the responsibilities and job description for the Sr. AI Technology Architect position at Wise Skulls Corp.?
Title: Sr. AI Technology Architect
Location: Irving, TX and Charlotte, NC
Duration: 6 months (possibility of an extension)
Implementation Partner: Infosys
End Client: To be disclosed (Banking & Finance)
JD :
Role Overview
We are looking for a visionary AI Senior Technology Architect to lead enterprise-scale AI transformation initiatives. This role requires deep expertise in Generative AI, Agentic AI systems, AI infrastructure, and cloud-native architectures, with a strong focus on delivering scalable, high-performance AI solutions and driving business impact. This role is critical to driving AI-first enterprise strategy, enabling next-generation capabilities through Agentic AI, LLM ecosystems, and edge intelligence while delivering measurable business value.
Key Responsibilities
• Define and govern enterprise AI architecture including LLMs, RAG, Agentic AI, and Edge AI systems.
• Establish reference architectures for cloud-native AI, GPU-based inferencing, and distributed workloads.
• Architect and deploy LLM-powered solutions using RAG, embeddings, vector databases, and orchestration frameworks.
• Design Agentic AI workflows leveraging tools such as LangChain, LangGraph, Azure AI, and Databricks.
• Lead AI infrastructure strategy including GPU optimization and high-performance compute environments.
• Build and scale AI platforms across AWS, Azure, and Google Cloud Platform ecosystems.
• Lead development of advanced AI/ML models across NLP, computer vision, graph ML, and forecasting domains.
• Architect Edge AI solutions for low-latency, distributed decision-making systems.
• Establish governance for responsible AI, security, and compliance.
• Mentor teams and drive innovation and capability development.
• Define and govern enterprise AI architecture including LLMs, RAG, Agentic AI, and Edge AI systems.
• Establish reference architectures for cloud-native AI, GPU-based inferencing, and distributed workloads.
• Architect and deploy LLM-powered solutions using RAG, embeddings, vector databases, and orchestration frameworks.
• Design Agentic AI workflows leveraging tools such as LangChain, LangGraph, Azure AI, and Databricks.
• Lead AI infrastructure strategy including GPU optimization and high-performance compute environments.
• Build and scale AI platforms across AWS, Azure, and Google Cloud Platform ecosystems.
• Lead development of advanced AI/ML models across NLP, computer vision, graph ML, and forecasting domains.
• Architect Edge AI solutions for low-latency, distributed decision-making systems.
• Establish governance for responsible AI, security, and compliance.
• Mentor teams and drive innovation and capability development.
Required Qualifications
• 15 years of experience in AI/ML, Data Science, or Technology Architecture.
• Strong expertise in Generative AI, LLMs, RAG, and Agentic AI systems.
• Proficient in Python, APIs, microservices, and data engineering frameworks.
• Experience with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization technologies.
• Deep understanding of AI infrastructure including GPU optimization and benchmarking.
• Proven ability to lead large-scale transformation programs.
• 15 years of experience in AI/ML, Data Science, or Technology Architecture.
• Strong expertise in Generative AI, LLMs, RAG, and Agentic AI systems.
• Proficient in Python, APIs, microservices, and data engineering frameworks.
• Experience with cloud platforms (AWS, Azure, Google Cloud Platform) and containerization technologies.
• Deep understanding of AI infrastructure including GPU optimization and benchmarking.
• Proven ability to lead large-scale transformation programs.
Preferred Qualifications & Experience
• Experience in banking, telecom, healthcare, energy, or supply chain domains.
• Exposure to Edge AI, O-RAN architectures, and distributed systems.
• Advanced degree (PhD/Master’s) in AI, Data Science, or related field.
• Experience in Enterprise adoption of AI platforms and architecture standards.
• Experience in Scalable deployment of AI solutions delivering measurable outcomes.
• Experience in banking, telecom, healthcare, energy, or supply chain domains.
• Exposure to Edge AI, O-RAN architectures, and distributed systems.
• Advanced degree (PhD/Master’s) in AI, Data Science, or related field.
• Experience in Enterprise adoption of AI platforms and architecture standards.
• Experience in Scalable deployment of AI solutions delivering measurable outcomes.