Technical Architect

We are looking for an architect whose core skill is integration. Years of making AI, data, cloud and enterprise systems work together reliably, and knowing from experience where they tend to fall apart and why. This is the person the team comes to when the integration is hard, and the right answer is not obvious. The role covers the whole path, from a business ask that arrives as one line through to technical direction, architecture, building the platform, and keeping it running in production. You will work with senior stakeholders on one side and engineering teams on the other, and you will be accountable for how the system behaves once it is live.

The candidate will have responsibilities across the following functions:

Architecture and direction:

• Take a business ask, often a single line, and turn it into a technical direction that senior stakeholders agree with and an engineering team can build from.
• Own the integration architecture: API contracts, data flow, failure modes, retry behaviour and observability.
• Define these before build starts and stay accountable for them through to production.
• Set the standards for API contracts, integration patterns and reusable assets, and make them good enough that other teams choose to adopt them.
• Write things down. API contracts, architecture decisions and runbooks, so the next person moves faster.

Platforms and integration:

• Build developer-facing API platforms, covering access tiers, authentication through OKTA, OAuth and long-lived credentials, gateway configuration and quota management.
• Connect the AI stack to real systems. FastAPI orchestration across Bedrock, vector search, document pipelines and analytics, with contracts and runbooks in place before anything goes live.
• Design event-driven pipelines on AWS using Lambda, Step Functions, EventBridge and Kafka, with dead letter queues, retry logic, structured logging and tracing built in from the start.

Production and delivery:

• Own the system in production. When something breaks, lead the triage, find the root cause, fix it properly and write the runbook.
• Guide frontend and infrastructure delivery. Enough React, Terraform and CI/CD knowledge to review the work and steer it, without writing production frontend code yourself.
• Work across network, infrastructure, product and business teams to get problems that cut across them resolved.

The core requirements for the job include the following:

Engineering and architecture:

• 6-10 or more years in engineering, including real time in architecture or technical leadership, owning how systems get designed and delivered.
• Integration as the main line of your experience: making AI, data, cloud and enterprise systems work together reliably at scale.
• Good to have JavaScript and Python at depth.
• Able to design, build and review production services on your own. AWS as your primary cloud: Lambda, EKS, Bedrock, SageMaker, Step Functions, EventBridge, IAM and VPC, with a clear view of what each choice costs to run.
• Infrastructure you have delivered yourself using Terraform, Docker, Kubernetes and CI/CD.
• Enough React to read a codebase, review the work and guide delivery.

AI and GenAI:

• Enough understanding of the AI stack to make integration and architecture decisions that hold up: model lifecycles, embedding pipelines, RAG, agent patterns and where latency comes from.
• Data science depth is not required.
• Practical knowledge of vector databases such as Pinecone, FAISS, Weaviate or Qdrant, and a view on which one suits a given use case and scale.
• Experience connecting an AI system into a production platform: ingestion, enrichment, post-processing, observability and failure handling.
• A point of view on responsible AI as an architecture problem, covering monitoring, bias detection and explainability.