Senior MLOps Engineer — Production CV & Edge Vision Lead

Attis; Boston (MA); On-site; USD 160,000 - 200,000; Full time

Job summary

Attis, a Boston-based Series B startup, is seeking an experienced MLOps Engineer to own the production ML stack for a live computer vision product.

You will migrate serverless prototypes to enterprise-grade Kubernetes infrastructure, architect cloud-native pipelines on GCP, and optimize edge vs cloud inference across devices including embedded probes. On-site four days a week in Boston.

Qualifications

• 5+ years of professional software/ML engineering experience, with significant tenure owning live production ML systems.
• Proven background taking multi-stage computer vision models from prototype to high-volume production serving.
• Strong production cloud engineering expertise within GCP (GKE, Cloud Run, Vertex AI, Cloud Functions, Pub/Sub) and Docker.

Responsibilities

• Own the end-to-end multi-stage computer vision inference pipeline across latency, throughput, reliability, and cost.
• Re-architect the serving stack onto containerized, auto-scaling GPU/CPU clusters using Triton, TorchServe, or Cloud Run.
• Build automated deployment pipelines with canary rollouts, shadow traffic evaluation, and rapid rollback mechanisms.
• Establish production monitoring infrastructure for data drift detection and real-world failure triage.
• Develop closed-loop active learning workflows, automated labeling pipelines, dataset versioning, and offline regression tests.
• Evaluate on-device versus cloud inference trade-offs for mobile capture probes using CoreML, TFLite, or ExecuTorch.
• Serve as the primary cloud architecture authority across GCP, Terraform IaC, and container standards.

Skills

ML engineering

Computer vision

GCP expertise

Model serving

Tools

Docker

Kubernetes

GKE

TensorRT

Job description

Attis, a Boston-based Series B startup, is seeking an experienced MLOps Engineer to own the production ML stack for a live computer vision product.

You will migrate serverless prototypes to enterprise-grade Kubernetes infrastructure, architect cloud-native pipelines on GCP, and optimize edge vs cloud inference across devices including embedded probes. On-site four days a week in Boston.