Remote: Yes

Willing to relocate: Yes (continental US)

Technologies: TypeScript/Node, Python, Java, AWS, GCP, vector embeddings, ECS, SQS, DynamoDB, MongoDB, Datadog, CDK, Terraform, Docker, Kubernetes

Resume: https://drive.proton.me/urls/XKGXBP4BBW#qxa0grxxCT8c

Email: sean_t_b@pm.me

Senior Engineer most recently at Anywhere Real Estate (contacts platform, thousands of agents): designed and shipped duplicate contact detection using vector embeddings and weighted similarity scoring, replacing brittle boolean rules with fuzzy/near-duplicate matching that actually works on messy CRM data. Led a full redesign of async sync infrastructure—moved from Lambda to ECS, introduced a queue-backed worker pool (SQS + dedicated containers), and decoupled task execution via a shared worker contract. Took P99 enqueue-to-dequeue from ~5s to <10ms; cut peak queue ingress ~100× with producer-side backpressure. These patterns map directly to agentic workflows and human-in-the-loop automation at scale.

Before that: region build orchestration at Amazon (correctness invariants, async message passing, safer cutovers); ML-powered OCR and machine translation pipelines at The Bison Group (GCP Document AI + Azure Translator, scaled to 2,000-page docs, resolved >70 RPS throttling, zero-downtime API migrations); FHIR validation on Lambda at Cigna.

Comfortable owning ambiguous backend/AI feature work end to end—architecture, implementation, observability, rollout. I use AI tooling (Cursor, Claude, ChatGPT) heavily for design spikes, API scaffolding, and test generation, with review and monitoring before anything hits production.

Looking for: a team building something with real technical depth, where async/distributed systems or AI-enabled product features are central, not bolted on. Startup or growth stage preferred but not required. Open to fully remote or onsite/hybrid anywhere in the continental US—prefer to start remotely and happy to discuss relocation once we've established a good fit.