AI Architect
Core
Design and implement end-to-end architectures for Generative AI and AI/ML solutions, translating architectural standards into production-ready systems for enterprise platforms.
Role type
Senior IC AI Architect (Generative AI & MLOps)
Builds
Enterprise AI platforms, reusable code templates, libraries, and accelerators for GenAI workloads.
Domain
EdTech, Generative AI, Cloud-Native AI, MLOps
Deliverable
production ML models
Required skills
GenAI architecture (RAG, vector search, prompt orchestration), LLM-based solutions, traditional ML pipelines, cloud AI services (Azure/AWS/GCP), vector databases, data pipelines for AI, MLOps/LMMOps practices, Python, AI frameworks (LangChain, Semantic Kernel, PyTorch, TensorFlow)
Preferred skills
Agentic workflows, model evaluation frameworks, AI observability, event-driven architectures, Azure/AWS/GCP certifications
Technologies
Azure OpenAI/AI Studio, AWS Bedrock/SageMaker, GCP Vertex AI, Azure AI Search, Pinecone, Weaviate, OpenSearch, pgvector, Spark, Databricks, Synapse, Snowflake, dbt, Airflow, LangChain, LangGraph, Semantic Kernel, PyTorch, TensorFlow, MLflow
Responsibilities
Design secure end-to-end AI solution architectures including data ingestion, model training, and inference pipelines; Build GenAI solutions using RAG and model evaluation frameworks; Create and maintain HLD/LLD documentation and sequence diagrams; Build secure reusable code templates and libraries for deployment and monitoring; Architect federated data access patterns and optimize vector DB schemas; Partner with security teams to implement governance, safety, and compliance controls; Provide technical guidance, mentoring, and code reviews for teams using AI services.
Seniority
Senior, hands-on IC