ABOUT THE ROLE :

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. AWS is the primary cloud environment, and working knowledge of Azure and GCP is also required.

WHAT YOU WILL DO :

Architecture and technical direction :

- Translate business asks into technical designs, align senior stakeholders, and own integration architecture end to end.

- Build internal developer platforms including access tiers, OAuth, Okta, and gateway configuration.

- Define and evolve integration patterns and reusable assets.

- Guide frontend and infrastructure delivery using React, Terraform, and CI/CD.

AI and data platform integration :

- Integrate AI stack using FastAPI, Bedrock, vector search, and document pipelines.

- Choose and implement vector databases (Pinecone, FAISS, Weaviate, Qdrant).

- Design AI safeguards including monitoring, bias detection, and explainability.

- Choose the right vector database for each use case across Pinecone, FAISS, Weaviate, and Qdrant, with a clear rationale for each choice.

- Integrate AI capabilities into production platforms end to end, covering ingestion, enrichment, post-processing, observability, and failure handling.

- Design responsible AI safeguards into the platform from the start, including monitoring pipelines, bias detection, and explainability. Should have a clear point of view on how to do this and why.

Cloud infrastructure and resilience :

- Design event-driven pipelines on AWS (Lambda, Step Functions, EventBridge, Kafka).

- Design event-driven pipelines on AWS using Lambda, Step Functions, EventBridge, and Kafka, with dead letter queues, retries, structured logging, and distributed tracing built in from the start.

- Know the AWS service landscape well. Choose the right AWS service for each problem across Lambda, EKS, Bedrock, SageMaker, Step Functions, EventBridge, IAM, and VPC, with clear awareness of the cost and operational trade-offs of each choice.

- Ship infrastructure through Terraform, Docker, Kubernetes, and CI/CD.

- Build security and observability into the platform from the start as first principles, including OAuth, SSO, zero-trust design, structured logging, and distributed tracing.

Production ownership :

- Lead production incidents, triage, and root cause analysis.

- Lead production incidents end to end, covering triage, root cause analysis, fixes, and runbook documentation.

- Drive cross-team issues to resolution when they span network, infrastructure, product, or business teams.

WHAT WE ARE LOOKING FOR :

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 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.

HOW YOU WORK :

- You move from ambiguous business asks to well-designed systems without needing the brief tightened first.

- You document API contracts, architecture decisions, and runbooks as you go, because it saves the team time later.

- You set the standard when one is missing. If teams across the business are solving the same problem differently, you create a pattern that holds and that others actually adopt.

- You take a clear position on technical trade-offs and bring stakeholders along, including engineering, product, and executive audiences.

NICE TO HAVE :

- Prior experience in a consulting or advisory architecture role across multiple industries or client environments.

- Background in media and entertainment, or other content-heavy industries where ingestion, rights, and metadata complexity are part of the day-to-day.

- Familiarity with MLOps practices covering model versioning, experiment tracking, governance, and controlled deployment of AI models and adapters.

- Experience designing or operating cost and FinOps controls for cloud and AI workloads at scale.

- Experience with identity and access patterns under strict audit or compliance regimes such as SOX, SOC 2, or equivalent.