AI Solution Architect - Agentic & Generative AI

AI Solution Architect – Agentic & Generative AI

Locations: Austin, Tx | Tempe, Az | Charlotte, NC | New York, NY

Client: LPL

Rate: As per market standard

Role Overview

The AI Solution Architect is a senior technical leader responsible for designing, architecting, and operationalizing Agentic AI and Generative AI solutions at enterprise scale. This role is central to shaping and implementing an AI-first Product Delivery Lifecycle (PDLC), ensuring product development processes, engineering practices, and operating models are optimized for AI-native platforms, agentic systems, and rapid value iteration.

This individual will operate at the intersection of architecture, AI platform engineering, ML lifecycle management, enterprise integration, governance, and organizational transformation.

Key Responsibilities

AI Architecture & Solution Design

• Architect enterprise-scale Agentic and Generative AI systems, including:
• Multi-agent orchestration frameworks
• Retrieval-Augmented Generation (RAG) pipelines
• Autonomous task execution patterns
• Tool-use integration frameworks
• Establish reference architectures for an AI-first PDLC covering:
• Design
• Development
• Testing & evaluation
• Deployment
• Monitoring & observability
• Risk controls & governance
• Design and implement model lifecycle pipelines including:
• Training & fine-tuning workflows
• Evaluation harnesses
• Model registry & versioning
• Continuous improvement loops
• Embed AI capabilities into client-facing platforms, internal tooling, and operational workflows.

AI-First PDLC Transformation

• Define architecture, tooling, and standards for an AI-native delivery lifecycle, including:
• Prompt engineering frameworks
• Agent design patterns
• Automated LLM evaluation systems
• Safety guardrails and policy enforcement
• Data quality validation mechanisms
• Integrate AI evaluation and governance gates into CI/CD pipelines.
• Establish best practices to enable cross-functional teams to become AI “builders.”
• Drive adoption of AI-driven development patterns across product, engineering, design, and risk functions.

Enterprise Integration & Data Strategy

• Design integrations between AI systems and core enterprise platforms.
• Partner with Data Engineering teams to define:
• Data ingestion architectures
• Embedding & vectorization strategies
• Feature stores
• Real-time inference pipelines
• Ensure architectural alignment with:
• Cloud strategy
• Enterprise data governance
• Security & compliance standards

Security, Compliance & Responsible AI

• Architect AI systems in compliance with regulatory and governance requirements.
• Embed identity, authorization, auditing, and model-level security patterns.
• Implement Responsible AI practices, including:
• Transparency
• Bias monitoring
• Fairness evaluation
• Performance & drift monitoring
• Auditability

Cross-Functional Leadership

• Partner with senior leaders across Product, Enterprise Architecture, DevSecOps, Infrastructure, and Operations.
• Lead architectural reviews and design whiteboarding sessions.
• Mentor engineering teams and contribute to AI architecture standards.
• Support hiring and talent development for emerging AI roles.

Required Qualifications

• 2+ years architecting Generative AI, Agentic AI, or ML systems at enterprise scale.
• 6+ years experience in cloud-native architecture (AWS preferred), including:
• Microservices
• Kubernetes
• Event-driven systems
• 5+ years hands-on experience with:
• LLMs
• Vector databases
• Embeddings
• Evaluation frameworks
• Guardrails
• Fine-tuning
• Orchestration frameworks
• 8+ years experience in Python.
• Proficiency in C#, Java, or TypeScript is a plus.
• 6+ years experience in ML Ops, including:
• CI/CD for ML
• Model versioning
• Monitoring
• Automated evaluation

Preferred Qualifications

• Bachelor’s degree in Computer Science, Engineering, AI/ML, or equivalent experience.
• Relevant certifications such as:
• AWS Solutions Architect – Professional
• AWS Machine Learning Specialty
• Terraform Associate
• Experience in regulated industries.
• Experience designing AI systems under compliance constraints.