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Senior back-end engineer

Systems that turn hard data into useful decisions.

I specialize in data pipelines, data analysis, and computer vision—designed for reliability, cost control, and real-world users.

Explore selected work ↓View resume ↗
Aaron Manning
Toronto, Canada

Scale

10,000 assessments / month

Efficiency

93%+ lower inference cost

Focus

Data, vision & reliable systems

Selected work

Technical ownership, end to end.

03 case studies

01

Computer vision

Video-assessment pipeline

Built the distributed system that turns training video into reliable movement feedback at production scale.

  • Coordinated video artifacts, cloud storage, Pub/Sub inference, external analysis services, and explicit state transitions.
  • Redesigned inference around idempotent output handling, bounded producer-consumer frame processing, and batched detection and pose estimation.
Outcome
10,000 assessments / month
Focus
93%+ lower inference cost

02

AI systems

AI planning & retrieval

Designed grounded AI workflows for training plans and semantic retrieval over a controlled exercise and assessment catalog.

  • Used agents, structured outputs, and tool calling to produce useful plans within explicit product boundaries.
  • Implemented relevance-ranked retrieval with embeddings, PostgreSQL/pgvector HNSW indexing, and Redis caching for low-latency results.
Outcome
Grounded, structured AI outputs
Focus
Low-latency semantic retrieval

03

Platform

Secure event-driven foundation

Architected the backend foundation for complex, multi-role workflows where correctness, observability, and safe failure matter.

  • Built typed API contracts, fine-grained authorization, verified payment webhooks, idempotent handling, and transactional fulfillment.
  • Established asynchronous workloads with outbox jobs, workers, retries, timeouts, audit feeds, notifications, and real-time events.
Outcome
Reliable multi-role workflows
Focus
Secure, observable operations

AI-native workflow

Use AI for leverage.
Keep engineering judgment accountable.

I use AI to accelerate architecture exploration and feature implementation, while retaining ownership of consequential decisions, acceptance criteria, and release readiness.

  1. FrameExplore options, then make system-level decisions deliberately.
  2. BoundBreak complex work into small, reviewable increments.
  3. AccelerateUse AI heavily for implementation, tests, and documentation.
  4. OwnSet acceptance criteria and make the final release decision.

Capabilities

Depth where systems meet.

Back-end & data

TypeScript, Node.js, Python, PostgreSQL, Redis, API design, transactions, caching, idempotency

AI & computer vision

Agents, structured outputs, embeddings, semantic retrieval, pose estimation, video processing, biomechanics

Cloud & reliability

GCP, Cloud Run, Cloud SQL, Pub/Sub, Terraform, Docker, Kubernetes, CI/CD, observability

Security & integration

Authorization, Stripe, webhooks, auditability, least privilege, credential management

Background

Engineering informed by movement science.

At Curv Health, I have worked across AI-native back-end systems, distributed computer-vision pipelines, and cloud infrastructure since 2021. Before that, I built human-motion analysis pipelines and computer-vision assessments as a movement scientist.

M.Sc., Biomechanics — McGill University · B.Sc., Engineering Physics — Queen’s University