Job Description

Role: AI Architect – Generative AI
Location: Tampa, FL (3 Days onsite/week)

Job Description:
Role focused on designing, implementing, and scaling enterprise-grade AI and Generative AI solutions.

Role Overview
The AI Architect – Generative AI will be responsible for defining the technical vision, architecture, and delivery approach for scalable AI solutions across enterprise environments. The role will focus on large language models, Retrieval-Augmented Generation, agentic AI frameworks, cloud-native AI platforms, MLOps/LLMOps, governance, security, and responsible AI adoption. The ideal candidate will work closely with business stakeholders, data scientists, engineering teams, product owners, and security teams to convert business needs into production-ready AI capabilities.
Key Responsibilities
•    Design end-to-end AI and Generative AI architectures aligned with business objectives, enterprise standards, and technology roadmaps.
•    Architect scalable solutions using large language models, Retrieval-Augmented Generation, embeddings, vector databases, prompt engineering, fine-tuning, and agentic AI patterns.
•    Define architecture blueprints, reusable patterns, solution accelerators, and reference implementations for enterprise AI adoption.
•    Lead technical evaluation and selection of AI platforms, model providers, orchestration frameworks, vector stores, cloud services, and integration technologies.
•    Design and operationalize LLMOps and MLOps pipelines covering model evaluation, prompt lifecycle management, monitoring, feedback loops, versioning, deployment automation, and continuous improvement.
•    Collaborate with data engineering teams to design ingestion, indexing, chunking, embedding, semantic search, and knowledge retrieval pipelines.
•    Ensure AI solutions are secure, reliable, scalable, observable, cost-effective, and compliant with enterprise governance and responsible AI standards.
•    Define guardrails for responsible AI usage, including data privacy, bias mitigation, explainability, auditability, human-in-the-loop controls, and prompt injection protection.
•    Provide technical leadership to AI engineers, data scientists, cloud engineers, and application development teams during design, build, testing, and production rollout.
•    Translate complex AI concepts into clear recommendations for business leaders and non-technical stakeholders.
•    Stay current with emerging AI technologies, frameworks, model capabilities, risks, and industry best practices.