[Hiring] AI Architect @Cambium Learning Group

Role Description

The AI Architect will design and implement end-to-end architectures for Generative AI and AI/ML solutions, working under the guidance of the Director Enterprise Systems to bring the organization’s AI strategy to life. This role is deeply technical, hands-on, and delivery-focused.

• Design secure end-to-end AI solution architectures including data ingestion, model training, inference pipelines, orchestration flows, and integration with downstream systems.
• Implement architectures defined by the Director Enterprise Systems, ensuring alignment with standards, patterns, and platform strategy.
• Build GenAI solutions using RAG, vector search, grounding strategies, prompt orchestration, and model evaluation frameworks.
• Create and maintain high-quality HLD/LLD documentation, sequence diagrams, and data flows for AI workloads.
• Perform hands-on technical proofs of concept, evaluate models/tools, and convert prototypes into production-grade systems.
• Build secure reusable code templates, libraries, and patterns for deployment, evaluation, and monitoring of workloads.
• Partner with engineers to integrate AI components into pipelines, data products, and operational workflows.
• Implement model lifecycle management: versioning, experimentation tracking, registry integration, automated deployment.
• Architect federated data access patterns for AI, integrating multiple source systems into cohesive retrieval pipelines.
• Design data pipelines that support AI use cases: feature engineering, embedding generation, chunking strategies, and retrieval flows.
• Implement and optimize vector DB schemas, embeddings, and hybrid search patterns.
• Ensure data quality, lineage, access controls, and privacy protections align with enterprise requirements.
• Partner with Security and Privacy teams to ensure AI solutions align with applicable regulations and standards.
• Apply responsible AI principles, ensuring solutions include safety, bias mitigation, grounding, and hallucination safeguards.
• Establish guardrails for model training and prompt usage, including restrictions on sensitive data ingestion.
• Implement enterprise-approved security patterns.
• Conduct architecture reviews, risk assessments, and model evaluations as part of deployment readiness.
• Collaborate with product managers, engineers, and business stakeholders to refine requirements.
• Provide clear technical guidance, mentoring, and code reviews for teams using AI services.
• Communicate trade-offs, limitations, and risks of different AI approaches to both technical and non-technical audiences.
• Ensure solutions meet high standards of quality, with successful delivery driven by thorough testing and validation practices.
• Provide technical guidance to other engineering team members, fostering growth and knowledge sharing.

Qualifications

• 5+ years of experience in engineering and architecture roles, operating at enterprise scale.
• Hands-on experience designing and implementing LLM-based solutions.
• Experience with traditional ML models and pipelines.
• Experience with cloud AI services (Azure OpenAI/AI Studio, AWS Bedrock/SageMaker, GCP Vertex AI).
• Experience with vector databases & search.
• Experience with data pipelines supporting AI.
• Strong understanding of MLOps/LMMOps practices.
• Proficiency in Python and familiarity with key AI frameworks.
• Working knowledge of security, governance, and compliance controls related to AI.

Requirements

• Experience operationalizing agentic workflows, copilots, or AI-enabled automation within enterprise environments.
• Hands-on experience with model evaluation frameworks.
• Experience implementing observability for AI.
• Familiarity with event-driven and microservices architectures.
• Certifications such as Azure AI Engineer Associate, Azure Solutions Architect Associate, AWS Machine Learning Specialty, GCP Professional ML Engineer.

Measures of Success

• Architecture deliverables aligned with standards set by the Senior Solutions Architect.
• Reduction in engineering effort through reusable components and accelerators.
• Successful deployment of AI workloads meeting performance, security, and cost criteria.
• Improved reliability and monitoring coverage for AI solutions.
• Demonstrated ability to operationalize GenAI solutions in production environments.

Example Day-to-Day Work

• Build a RAG pipeline for a business unit using enterprise vector search.
• Assist engineering teams adopting new AI building blocks.
• Run model evaluations to compare open-weight vs. hosted models for a specific workload.
• Draft LLD diagrams for an AI-powered automation feature.
• Implement observability dashboards tracking grounding quality, latency, and cost per inference.