AI Architect

ICW Group•San Diego, CA

2d•Hybrid

About The Position

The AI Architect II is a senior technical leadership role focused on modernizing the company’s technology platform. The position provides thought and technical leadership, influences cloud and AI strategy, and works hands-on to deliver solutions with business impact. The role helps shape the direction of a rapidly evolving organization and guides its cloud and AI transformation. The position establishes enterprise architecture for AI solutions and cloud platforms, including responsible AI practices, MLOps, AWS architectures, and integration patterns. It collaborates with business and technology stakeholders, guides engineering teams, evaluates emerging technologies, and supports modernization and application rationalization initiatives. This position has no supervisory responsibility. The role operates in an office environment and frequently uses a computer, telephone, copier, and other standard office equipment. Employees are regularly required to sit, walk, stand, talk, and hear. The posting states that employment sponsorship is not offered. The salary range is not provided.

Requirements

Bachelor’s degree in Computer Science, Information Systems, or a related field.
At least 5–7 years of experience with cloud technologies and IT systems, including demonstrated expertise in enterprise-scale solution design and delivery.
At least 2–3 years of hands-on experience with AI/ML technologies, including practical application of LLMs, agentic frameworks, or AI/ML services in a cloud environment.
Strong understanding of AI concepts, including LLMs, AI agents, agentic frameworks, Model Context Protocol (MCP), Agent-to-Agent (A2A) communication, and prompt engineering.
Hands-on experience with AWS AI/ML services, including Amazon Bedrock, SageMaker, Kendra, and Amazon Q Business.
Familiarity with LLM fine-tuning, embedding models, vector stores, and chunking strategies for RAG pipelines.
Experience with AI orchestration frameworks such as LangChain, LangGraph, and AWS Strands Agents for multi-step agentic workflows.
Knowledge of frontier model evaluation, benchmarking methodologies, and model selection criteria for responsible and cost-effective AI adoption.
Familiarity with open-source AI tools such as Open WebUI and LiteLLM for unified LLM API gateway and proxy management.
Understanding of responsible AI principles, including bias detection, model risk management, content filtering, PII handling, and data residency.
Familiarity with MLOps tooling such as SageMaker Pipelines or MLflow for model versioning, experiment tracking, and drift detection.
Knowledge of LLM function calling, structured output patterns, and tool integration techniques for reliable agentic systems.
Familiarity with multimodal AI use cases, including document understanding, image analysis, and audio processing.
Understanding of AI cost and performance optimization, including token management, prompt caching, model quantization, and inference workload right-sizing.
Deep knowledge of AWS cloud services, with emphasis on serverless architectures and design patterns.
Proficiency in Linux and Windows system administration, including networking, storage, security, and scripting.
Proficiency in Python for scripting, automation, and cloud-native application development.
Hands-on experience with Snowflake, AWS Aurora (PostgreSQL), DynamoDB, Glue, Redshift, Athena, S3-based data lakes, and data pipeline design.
Experience with infrastructure-as-code tools such as AWS CDK, CloudFormation, or Terraform.
Knowledge of enterprise integration patterns, RESTful API design, event-driven architecture, and microservices design principles.
Familiarity with DevSecOps practices, including CI/CD pipelines, automated testing, container orchestration (ECS, EKS), and security-by-design.
Understanding of cloud cost management, FinOps principles, and architectural trade-off analysis.
Experience with observability and monitoring tools such as Amazon CloudWatch, AWS X-Ray, and third-party APM solutions.
Strong communication skills, including the ability to present complex technical concepts to technical and non-technical audiences, including executive leadership.

Nice To Haves

TOGAF certification.
Zachman Framework certification.
FEAF certification.
AWS Solutions Architect Associate certification.
AWS Solutions Architect Professional certification.
AWS Machine Learning – Specialty certification.
SnowPro Core certification.
SnowPro Advanced Architect certification.
Amazon Bedrock Guardrails experience.

Responsibilities

Establish architectural standards, patterns, and reference architectures for AI solutions, including RAG pipelines, agentic workflows, model serving, and AI gateway design.
Lead the evaluation, selection, and integration of AI/ML services and frameworks, including LLMs, AI agents, and multi-agent systems using protocols such as MCP and A2A.
Establish and enforce responsible AI practices, including model risk management, content filtering, and data privacy guardrails.
Define and oversee MLOps practices for reliable model deployment, monitoring, and lifecycle management across the enterprise.
Design enterprise AI patterns, including AI gateway architecture, knowledge bases, and integration of AI capabilities into existing business systems and workflows.
Design and implement scalable cloud architectures on AWS, leveraging serverless technologies and design patterns.
Define and enforce cloud architecture standards, patterns, and guardrails to promote consistency, security, and cost optimization.
Collaborate with business and technology stakeholders to translate business requirements into technical architecture solutions aligned with enterprise strategy.
Lead systems design efforts involving microservices, API design, event-driven systems, and integration patterns across cloud-native and hybrid environments.
Evaluate emerging AI technologies and recommend adoption strategies, including generative AI, agentic workflows, and intelligent automation.
Develop and maintain architecture documentation, reference architectures, and technology roadmaps for cloud and AI transformation.
Provide technical mentorship and guidance to engineering teams on cloud best practices, AI implementation patterns, and modern software design principles.
Conduct architecture reviews, risk assessments, and proof-of-concept initiatives to validate technology decisions and drive innovation.
Lead application rationalization initiatives to assess, consolidate, and modernize the application portfolio, reduce technical debt, and align technology investments with business priorities.