Enterprise Architect

We are looking for an Enterprise Architect to lead the technical direction for AI-enabled, cloud-native platforms in production. You will translate business asks into buildable designs, own integration architecture end to end, and set the patterns that other engineering teams build on.

Experience and technical depth

• 10-15 years in engineering, with substantial time in architecture or principal-level technical leadership roles. Someone who has had real ownership of how systems are designed and delivered.
• Integration is the core skill here. Years of making AI, data, cloud, and enterprise systems work together reliably, with a genuine understanding of where they fail.
• Production-depth experience with Python and FastAPI, including service design and code review.
• Depth across AWS services including Lambda, EKS, Bedrock, SageMaker, Step Functions, EventBridge, IAM, and VPC, with awareness of the cost and operational implications of each choice.
• Hands-on experience shipping infrastructure through Terraform, Docker, Kubernetes, and CI/CD as part of actual delivery.
• A track record of treating security and observability as design defaults, covering OAuth, SSO, zero-trust, structured logging, and distributed tracing.
• Enough React knowledge to review a codebase and guide delivery. Writing production frontend code is not required.
• Hands-on experience with Kafka or other production streaming platforms for event-driven architectures.
• Experience designing developer-facing API platforms at scale, including access tiers, gateway configuration, and quota controls.

Multi-cloud fluency

• Working knowledge across SageMaker, Lambda, API Gateway, IAM, and Glue for scalable AI/ML, serverless, and data engineering pipelines.
• Working knowledge of Azure across Azure OpenAI Service, AKS, API Management, Entra ID, and Data Factory for enterprise integration scenarios.
• Working knowledge of GCP across Vertex AI, BigQuery, Cloud Run, Pub/Sub, and Apigee.
• A clear understanding of the cost and operational trade-offs across cloud providers.

AI and GenAI

• Working fluency across LLM lifecycles, embedding pipelines, RAG, multi-agent patterns, and inference latency. A data science background is not required.
• Familiarity with vector databases including Pinecone, FAISS, Weaviate, and Qdrant, with a clear sense of when to use each.
• Experience integrating AI systems into production platforms end to end, covering ingestion, enrichment, post-processing, observability, and failure modes.
• A clear point of view on responsible AI as an architecture concern, including how to design monitoring pipelines, bias detection, and explainability into the platform from the start.