AI Solution Architect

Must be Local to Florida.

Experience: 8–15 years of overall IT experience, with at least 3–5 years focused on AI solution architecture and delivery. 

Skills Preferred: AI Solution Architecture, AI solution design, latest AI models, LLMs, enterprise AI architecture, cloud AI/ML platforms, data & MLOps integration 

Nice to Have Skills: responsible AI, AI governance, vector databases, RAG, semantic search, MLOps tools, cloud-native architecture, microservices, Kubernetes, agile delivery

Job Summary:

This role will closely collaborate with business stakeholders, product owners, data teams, and engineering to translate business requirements into scalable, secure, and robust AI architectures. 

Key Responsibilities:

• Lead the architecture, design, and technical roadmap for AI and AI-native solutions aligned to Advantive’s business strategy. 
• Translate business and functional requirements into scalable AI solution architectures, covering data, model, application, and integration layers. 
• Evaluate, select, and integrate latest AI models and LLMs (including cloud and third-party services) into enterprise applications and workflows. 
• Define reference architectures, patterns, standards, and reusable components for AI solution delivery across the organization. 
• Collaborate with data engineers, MLOps engineers, application developers, and product teams to ensure high-quality, production-grade AI deployments. 

Required Skills:

• Strong experience in AI Solution Architecture, designing and delivering enterprise-grade AI solutions. 
• Proven expertise in architectural design involving AI solutions, including end-to-end solution blueprints and reference architectures.
• Hands-on knowledge of designing AI-based solutions using machine learning, deep learning, and LLM-based approaches. 
• In-depth understanding of latest AI models and large language models (LLMs), including their capabilities, limitations, and suitable use cases. 
• Experience with AI/ML platforms and services (e.g., Azure AI, AWS AI/ML, Google Cloud AI, or equivalent). 
• Solid understanding of data architecture concepts, including data pipelines, feature stores, model deployment, and monitoring (MLOps). 
• Strong background in application integration patterns (APIs, microservices, event-driven architecture) for embedding AI into products and workflows. 

Good to Have Skills

• Experience with AI governance, model risk management, and responsible AI practices (fairness, explainability, security, and privacy). 
• Familiarity with vector databases, semantic search, RAG (Retrieval-Augmented Generation), and knowledge-graph-based solutions. 
• Experience in designing multi-tenant, cloud-native architectures using containers and orchestration (Docker, Kubernetes). 
• Knowledge of enterprise integration with ERP/CRM/line-of-business applications. 

Educational Qualification

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related discipline from a recognized institution.