The AI Architect will design and implement end-to-end architectures for Generative AI and AI/ML solutions. The ideal candidate has strong experience with data-driven AI, GenAI architectures, cloud-native patterns, and MLOps/LMMOps tooling.

Company: Cambium Learning

Role: AI Architect

Location: Remote

Experience:

5+ years of experience in engineering and architecture roles, operating at enterprise scale and demonstrating sustained ownership of complex data and AI enabled platforms.

Key Skills:

LLM-based solutions (RAG, tool use, prompt workflows, fine-tuning)
Traditional ML models and pipelines
Cloud AI services (Azure OpenAI/AI Studio, AWS Bedrock/SageMaker, GCP Vertex AI)
Vector databases & search (Azure AI Search, Pinecone, Weaviate, OpenSearch, pgvector)
Data pipelines supporting AI (Spark, Databricks, Synapse, Snowflake, dbt, Airflow)
MLOps/LMMOps practices
Python
LangChain/LangGraph
Semantic Kernel
PyTorch or TensorFlow
MLflow

Qualification:

Azure AI Engineer Associate
Azure Solutions Architect Associate
AWS Machine Learning Specialty
GCP Professional ML Engineer

Role Focus:

Design secure end-to-end AI solution architectures
Implement architectures defined by the Director Enterprise Systems
Build GenAI solutions using RAG, vector search, grounding strategies, prompt orchestration, and model evaluation frameworks
Create and maintain high-quality HLD/LLD documentation
Perform hands-on technical proofs of concept
Build secure reusable code templates, libraries, and patterns
Partner with engineers to integrate AI components into pipelines
Implement model lifecycle management
Architect federated data access patterns for AI
Design data pipelines that support AI use cases
Implement and optimize vector DB schemas
Partner with Security and Privacy teams to ensure AI solutions align with applicable regulations and standards
Apply responsible AI principles
Establish guardrails for model training and prompt usage
Conduct architecture reviews, risk assessments, and model evaluations
Collaborate with product managers, engineers, and business stakeholders
Provide clear technical guidance, mentoring, and code reviews

Nice to have:

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

Other:

Remote First Work Environment with flexible work arrangements
Reimbursement to help cover the cost of setting up your home or remote office
Equal Opportunity Employer with commitment to diversity and inclusion
Reasonable accommodations for qualified individuals with disabilities

AI Architect

Skills Required

Description