Senior AI Data Architect

Qualifications

• Notes:Pleasereview the MUST HAVE skills before applying
• Experience: 6-8 years in AI/ML engineering or architecture roles
• Core Requirement: Built and scaled an AI platform end-to-end
• Hands-On: 70-80% coding
• Generative AI Expertise:
• LLMs, Agentic AI, Retrieval-Augmented Generation (RAG)
• Amazon Bedrock and related AWS AI services
• MUST HAVE
• Cloud: Strong AWS architecture and integration skills
• Additional: Knowledge of vector databases, prompt engineering, and data governance
• Advanced Data Solutions & Engineering
• Technology Leadership & Best Practices
• Champion hands-on experimentation and rapid solution delivery whilemaintainingtechnical excellence
• 10+ years of experience in enterprise data architecture or engineering, with a strong hands-on focus on AI and cloud-native data platforms
• Proven experience in designing, implementing, andoptimizinglarge-scale AI systems, including LLM-based,GenAI, and agentic AI applications
• Expertisein Python,SQL,and modern data frameworks (e.g.,PySpark,Airflow,Snowflake,LangChain, Hugging Face, Vertex AI, OpenAI)
• Strong background indata modelling,distributed systems, and cloud architecture (AWS, GCP, or Azure)
• Experience developing and deploying AI/ML/GenAIpipelinesleveragingvector databases and RAG frameworks
• Bachelor's or
• Master's degree in Computer Science, Engineering, Data Science, or related field

Responsibilities

• The Senior Data Architect (AI Solutions) leads the design and implementation ofcutting-edgeAI platforms and data systems
• Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems
• Build and integrate cloud-native data pipelines using tools such as Snowflake, Airflow, and Vertex AI
• Implement retrieval-augmented generation (RAG) pipelines and multimodal data solutions
• Drive automation, observability, and performance optimization across AI and data workflows
• Lead initiatives to explore,validate, and scale emerging AI technologies
• Translate research and prototypes into production-ready capabilities
• Collaborate across teams to embed AI-driven insights and automation into business processes
• Evaluate and shape next-generation AI trends, including agentic systems and autonomous workflows
• Define and promote engineering standards that balance agility, scalability, and governance
• Collaborate with security, compliance, and governance partners to ensure responsible data and AI usage
• Mentor engineers and architects in modern data and AI development practices
• Act as a trusted advisor for business and technology leaders on data-driven innovation
• Lead internal workshops and training sessions to accelerate AI adoption
• Represent the organization in external forums, conferences, and publications focused on data and AI innovation

Full Description

Title: Senior Data Architect (AI Solutions)

Location: Raleigh, NC (2 days per week in office) North Hills

Type: Full Time Position

Notes:Pleasereview the MUST HAVE skills before applying

Top Must Have Skills:

• Experience: 6-8 years in AI/ML engineering or architecture roles.

• Core Requirement: Built and scaled an AI platform end-to-end.

• Hands-On: 70-80% coding

• Generative AI Expertise:

• LLMs, Agentic AI, Retrieval-Augmented Generation (RAG). - MUST HAVE

• Amazon Bedrock and related AWS AI services. - MUST HAVE

• Cloud: Strong AWS architecture and integration skills.

• Regulatory Exposure: Any regulated industry experience (finance, healthcare, pharma, etc.) preferred.

• Additional: Knowledge of vector databases, prompt engineering, and data governance.

Overview

The Senior Data Architect (AI Solutions) leads the design and implementation ofcutting-edgeAI platforms and data systems.

This role combines strong hands-on engineering with strategic innovation to design, prototype, and deliver intelligent, data-driven solutions that power analytics, machine learning, and next-generation AI applications.

This rolecollaboratesclosely with business and technology teams to turn ideas into working solutions,enabling faster insights, better decisions, and enterprise-wide innovation through data.

Responsibilities

Advanced Data Solutions & Engineering

Prototype and operationalize advanced AI solutions, including GenAI and LLM-based systems.

Build and integrate cloud-native data pipelines using tools such as Snowflake, Airflow, and Vertex AI.

Implement retrieval-augmented generation (RAG) pipelines and multimodal data solutions.

Drive automation, observability, and performance optimization across AI and data workflows.

Innovation & Applied AI

Lead initiatives to explore,validate, and scale emerging AI technologies.

Translate research and prototypes into production-ready capabilities.

Collaborate across teams to embed AI-driven insights and automation into business processes.

Evaluate and shape next-generation AI trends, including agentic systems and autonomous workflows.

Technology Leadership & Best Practices

Champion hands-on experimentation and rapid solution delivery whilemaintainingtechnical excellence.

Define and promote engineering standards that balance agility, scalability, and governance.

Collaborate with security, compliance, and governance partners to ensure responsible data and AI usage.

Mentor engineers and architects in modern data and AI development practices.

Collaboration & Knowledge Sharing

Act as a trusted advisor for business and technology leaders on data-driven innovation.

Lead internal workshops and training sessions to accelerate AI adoption.

Represent the organization in external forums, conferences, and publications focused on data and AI innovation.

Qualifications

• 10+ years of experience in enterprise data architecture or engineering, with a strong hands-on focus on AI and cloud-native data platforms.

• Proven experience in designing, implementing, andoptimizinglarge-scale AI systems, including LLM-based,GenAI, and agentic AI applications.

• Expertisein Python,SQL,and modern data frameworks (e.g.,PySpark,Airflow,Snowflake,LangChain, Hugging Face, Vertex AI, OpenAI)

• Strong background indata modelling,distributed systems, and cloud architecture (AWS, GCP, or Azure).

• Experience developing and deploying AI/ML/GenAIpipelinesleveragingvector databases and RAG frameworks.

• Bachelor's orMaster's degree in Computer Science, Engineering, Data Science, or related field.

Preferred:

• Experience with agentic AI design patterns, including tool-use orchestration, autonomous workflow agents, or AI copilots.

• Proficiencyin API design, microservices, and containerization (Docker, Kubernetes).

• Demonstrated ability to rapidly prototype new AI concepts and transition successfulPoCsinto production-grade systems.