Provide oversight for architecture assessment and design for infrastructure, information or integration domains that provide core capabilities for the enterprise.
Lead Architecture design of end-to-end enterprise integrated systems that serves multiple business functions.
Lead the design and implementation of enterprise data model and metadata structures for complex projects.
Drive architecture and design patterns for various AI and ML related models, and services.
Passion for applying state-of-the art AI algorithms to real-world problems.
Stay up to date with the latest developments in AI and related fields, evaluate new developments and technologies for potential integration into existing and future architecture.
Initiate and deliver technology evaluation and recommendations.
Develop and maintain current, planned, and future state architecture blueprints.
Lead in the identification and analysis of enterprise business drivers and requirements that drive the future state architecture.
Design and architect scalable AI/ML infrastructure solutions that support enterprise-wide AI initiatives, including model deployment, orchestration, and monitoring systems.
Establish technical standards and frameworks for AI application development, including agent architectures, RAG pipelines, and multi-modal AI systems.

Requirements

Four-year bachelor’s degree from an accredited college or university in a Computer Science or Information Technology
Expertise with modern techniques for Language Understanding, Computer Vision, and other Machine Learning and AI technologies
In-depth understand and expertise in one or more of the following: massive real-time event and message processing, data lakes, Big Data transformations and analytics, complex e-commerce applications, IoT edge and cloud data processing, SQL, and NoSQL data modeling.
Agile SDLC implementation in public cloud eco-system including environments management, peer review, CI/CD, resource optimization, etc.
Experience designing and implementing production AI agent systems using frameworks like LangChain, LangGraph, Semantic Kernel, or similar orchestration tools
Understanding of vector databases, embedding models, and semantic search architectures for RAG applications
Knowledge of GPU compute infrastructure, model serving platforms (NVIDIA NIM, TensorFlow Serving, vLLM), and inference optimization techniques
Familiarity with prompt engineering, fine-tuning approaches, and evaluation frameworks for LLM-based applications
Knowledge of emerging AI frameworks including multi-agent systems, autonomous agents, and AI-powered workflow automation.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

AI algorithmsMachine LearningLanguage UnderstandingComputer Visiondata modelingSQLNoSQLAgile SDLCprompt engineeringinference optimization

Soft Skills

leadershipcommunicationorganizationalanalyticalproblem-solving

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in Information Technology