AI Solution Architect

AI Solution Architect#26-17941 Up to $73.68 per hour Plano, TX Onsite Job Description Our client, a Banking company, is looking for a AI Solution Architect for their Plano, TX/Charlotte, NC location.

Responsibilities:

The AI Solution Architect responsible for designing, governing, and overseeing the implementation of scalable AI systems and solutions. This role bridges business requirements and technical execution by defining end-to-end AI architecture, ensuring solutions are secure, compliant, and aligned with enterprise strategy AI Architecture Design & Strategy Define end-to-end AI architecture using Bank AI approved tools, including data, models, integration, and deployment patterns Design scalable, resilient, and secure AI systems aligned with enterprise IT strategy Select appropriate tools, platforms, and frameworks for AI/ML workloads

Solution Design & Implementation:

Design, develop, and implement AI/ML models and systems for business use cases Translate business requirements into technical architecture and solution designs Ensure integration of AI solutions with existing systems, applications, and workflows

Data & Model Lifecycle Management:

Define data architecture, pipelines, and feature engineering requirements Establish model development, validation, deployment, and monitoring frameworks Ensure scalable, repeatable, and efficient model lifecycle processes

Governance, Security & Compliance:

Establish AI governance frameworks, including model versioning, documentation, and auditability Ensure compliance with data privacy, security, and regulatory requirements Implement safeguards for bias, explainability, and ethical AI practices

Performance Optimization & Monitoring:

Continuously evaluate AI system performance and optimize models and infrastructure Monitor model drift, accuracy, and operational efficiency Implement observability, logging, and alerting mechanisms

Technical Leadership & Collaboration:

Provide architectural guidance to data scientists, ML engineers, and developers Lead design reviews and enforce best practices across AI projects Collaborate with business leaders, product teams, and cross-functional stakeholders

Team Enablement, Training & AI Adoption:

Demonstrate strong people leadership and collaboration skills across technical and business teams Take ownership of training, mentoring, and guiding teams on effective use of AI tools and platforms Drive enterprise adoption of AI by embedding best practices, usage patterns, and hands-on coaching Act as an AI evangelist—helping teams understand where and how to apply AI effectively in daily workflows

Innovation & Emerging Technology:

Evaluate and adopt emerging AI technologies (e.g., Generative AI, LLMs, agent-based systems) Define architecture patterns for new capabilities like RAG, AI agents, and automation workflows Drive continuous improvement in AI engineering practices

Requirements:

Strong understanding of AI/ML concepts (ML, deep learning, NLP, Generative AI) Experience with multiple frameworks Expertise in multiple cloud platforms and MLOps practices Knowledge of data engineering, ETL pipelines, and big data technologies Understanding of APIs, microservices, and distributed systems design

Architecture & Engineering Skills:

System design and architecture (scalability, reliability, performance) AI pipeline design (training, inference, deployment, monitoring) Data architecture and model integration strategies Security, compliance, and governance frameworks Strong problem-solving and analytical skills Stakeholder communication (technical and non-technical audiences) Ability to lead and mentor engineering and data teams Microstrategy Microsoft Windows MS Windows Server