About the Job/Role

Aurigo is seeking an AI Adoption and Solution Architect to lead the design and implementation of AI/ML, Gen AI, and agentic AI initiatives. This role focuses on building AI pipelines, evaluation frameworks, and LLMOps architectures to enable AI adoption across Aurigo’s enterprise SaaS platform, Masterworks. The architect will collaborate with product and engineering teams to integrate AI capabilities, optimize AI workflows, and support scalable AI-driven solutions for infrastructure management.

Roles & Responsibilities

Design and implement AI/ML, Gen AI, and agentic AI solutions, including multi-agent frameworks and autonomous AI systems.
Architect AI adoption strategies across Aurigo products, ensuring smooth integration with existing platforms.
Build and optimize LLMOps/MLOps pipelines for AI models and agentic systems.
Collaborate with cross-functional teams to integrate AI solutions into cloud-native and hybrid architectures, including microservices, RESTful APIs, and containerized environments (Docker, Kubernetes).
Implement scalable deployment strategies for LLMs and AI agents in production, ensuring efficiency, reliability, and performance.
Evaluate and optimize LLMs (e.g., GPT, Claude, Mistral) for memory efficiency, inference speed, and resource usage.
Provide technical guidance on planning algorithms, decision-making models, and agentic architectures.
Work with data integration technologies and patterns (ETL/ELT, CDC) to support AI workflows.
Mentor teams and promote best practices in AI adoption and solution architecture.

Required Qualifications

15+ years of progressive experience in AI/ML, with a strong track record as a solution architect.
5+ years experience with cloud data platforms, particularly AWS data services.
3+ years experience architecting MPP database solutions (e.g., Redshift, Snowflake).
Hands-on experience with autonomous AI tools and frameworks (LangChain, Autogen, CrewAI, or custom agentic frameworks).
Proven experience with LLMOps/MLOps pipelines (e.g., Kubeflow, MLflow) and scalable AI deployments.
Strong expertise with LLMs (e.g., GPT, Claude, Mistral).
Deep understanding of planning algorithms, decision-making models, and agentic architectures.
Experience in cloud-native and hybrid system architecture, microservices, REST APIs, and containerization (Docker, Kubernetes).
Familiarity with on-premises and cloud AI deployments (AWS).
Bachelor’s or Master’s degree in Computer Science or a related field.

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