AI Architect – Generative & Agentic AI

This is a fully remote position, open to applicants in Texas.

📋 Description

• Take ownership of the complete architecture for enterprise Generative AI and Agentic AI solutions from inception to production.

• Spearhead the rapid development of proof-of-concept and MVP initiatives to assess AI use cases and mitigate technical risks.

• Design scalable AI platforms utilizing LLMs, RAG pipelines, vector databases, and multi-agent orchestration frameworks.

• Create API-first REST/GraphQL architectures that expose AI functionalities to downstream applications and enterprise systems.

• Establish technology selection criteria, architectural standards, and best practices for prompt engineering, model evaluation, AI governance, and Responsible AI.

• Architect and direct MLOps/LLMOps methodologies for deployment, monitoring, and management of model/agent lifecycles.

• Lead architecture reviews and showcase designs to executive stakeholders and engineering teams.

• Foster alignment among stakeholders.

• Mentor AI engineers, establish coding and architectural standards, and elevate the technical proficiency of the team.

• Assess and choose cloud-native AI services while considering scalability, cost, security, and performance.

⛳️ Requirements

• Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related discipline.

• Over 6 years of comprehensive IT experience encompassing software engineering, cloud architecture, and/or AI/ML.

• At least 3 years of practical architecture experience specifically in Generative AI / Agentic AI systems.

• Strong proficiency in Python and contemporary AI development frameworks.

• Proven history of architecting solutions using LLMs (OpenAI, Claude, Gemini, Llama, or open-weight models).

• In-depth knowledge of RAG architectures, vector databases (Pinecone, FAISS, Databricks Vector Databases, pgvector), and embedding models.

• Experience in architecting solutions on at least one major cloud platform (Azure, AWS, or GCP), including native AI services.

• Established expertise with MLOps/LLMOps: CI/CD, containerization (Docker/Kubernetes), observability, and evaluation frameworks.

• Solid foundation in AI governance, security, compliance, and Responsible AI practices.

• Exceptional communication skills, capable of presenting architecture to both executive leaders and engineers.

• Knowledge of the healthcare sector is highly desirable; experience with healthcare distribution, specialty pharma data, or healthcare EMR/EHR systems (HL7, FHIR, claims, NDC-level data) is a significant asset.

🏝️ Benefits

• Medical, dental, and vision care.

• Backup dependent care.

• Adoption assistance.

• Infertility coverage.

• Family building support.

• Behavioral health solutions.

• Paid parental leave.

• Paid caregiver leave.

• Training programs.

• Professional development resources.

• Mentorship programs.

• Employee resource groups.

• Volunteer activities.