MLOps / Cloud Deployment Engineer at Evisort

Vancouver, British Columbia, Canada

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Not SpecifiedCompensation

Mid-level (3 to 4 years), Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Healthcare, Dentistry, AIIndustries

Requirements

Strong experience with cloud platforms (AWS, GCP, or Azure) and GPU-based workloads (PyTorch, TensorFlow, CUDA)
Proficiency with Docker, FastAPI, and Linux-based deployment
Experience with CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation)
Knowledge of scalable architecture patterns (microservices, queues, autoscaling)
Competence with monitoring tools (Prometheus, Grafana, or equivalent)

Responsibilities

Containerize FastAPI backend and AI modules to create reproducible environments for inference and preprocessing pipelines
Set up CI/CD workflows (GitHub Actions, GitLab CI, or similar) for seamless deployment
Deploy backend services to cloud platforms (AWS, GCP, or Azure) using Cloud Run, ECS Fargate, Kubernetes, or similar technologies
Configure autoscaling, load balancing, and secure API endpoints for robust, scalable performance
Manage GPU-enabled instances for efficient AI inference
Integrate object storage (S3 / GCS) and databases (PostgreSQL / MongoDB) for model weights, user uploads, and job tracking
Implement asynchronous processing pipelines (Celery / Redis / PubSub) to optimize GPU utilization
Monitor runtime performance, logging, and uptime using tools like Prometheus or Grafana
Analyze cloud costs and performance, recommending optimizations and long-term architecture improvements
Ensure secure data handling, HTTPS configuration, and compliance with privacy best practices

Skills

Key technologies and capabilities for this role

MLOpsFastAPIDockerKubernetesCI/CDGitHub ActionsGitLab CIAWSGCPAzureCloud RunECS FargateGPUS3GCSPostgreSQLMongoDBCeleryRedisPubSubPrometheusGrafana

Questions & Answers

Common questions about this position

Is this role remote or on-site?

This is an on-site position at the Vancouver Office located at 675 W Hastings St.

What skills are required for this MLOps / Cloud Deployment Engineer role?

Required skills include strong experience with cloud platforms (AWS, GCP, or Azure) and GPU-based workloads (PyTorch, TensorFlow, CUDA), proficiency with Docker, FastAPI, and Linux-based deployment, experience with CI/CD pipelines and infrastructure-as-code (Terraform, CloudFormation), knowledge of scalable architecture patterns, and competence with monitoring tools like Prometheus or Grafana.

What is the salary or compensation for this position?

This information is not specified in the job description.

What does the company culture or work environment look like at Evisort?

This information is not specified in the job description.

What makes a strong candidate for this role?

A strong candidate will have hands-on experience with cloud platforms, Docker, FastAPI, CI/CD, and monitoring tools, plus nice-to-haves like MLOps frameworks (Kubeflow, MLflow), 3D data knowledge, and a passion for AI optimization.

Evisort

AI-powered contract analytics and management

About Evisort

Evisort provides AI-powered contract analytics and lifecycle management services to help businesses manage their contracts more efficiently. The company's technology has been trained on over 11 million contracts, allowing it to analyze a vast amount of contract data with high accuracy. This capability enables clients to search and review contracts significantly faster, reportedly improving efficiency by over 93%. Evisort serves a diverse range of clients, including sales operations directors, CFOs, and legal operations directors from various industries, helping them focus on closing deals instead of dealing with paperwork. Unlike traditional contract management solutions, Evisort's services also provide insights into contractual security risks and streamline vendor audits. The goal of Evisort is to protect and accelerate its clients' businesses by reducing the time spent on contract management and minimizing risks.

San Mateo, CaliforniaHeadquarters

2016Year Founded

$151.4MTotal Funding

SERIES_CCompany Stage

Data & Analytics, AI & Machine Learning, LegalIndustries

51-200Employees

Benefits

Unlimited PTO

Medical, dental, & vision

Remote-first work

Holiday shutdown

Wellness resources

Team events

Risks

Workday acquisition may disrupt existing client relationships and service delivery.

Partnership with Icertis may create conflicts of interest affecting market positioning.

Document X-Ray(TM) may face adoption resistance or technical issues impacting satisfaction.

Differentiation

Evisort uses AI to analyze over one billion contract data points accurately.

Evisort's Document X-Ray(TM) offers unprecedented visibility into contract agreements.

Evisort's AI technology transforms legal challenges into strategic opportunities for businesses.

Upsides

Evisort's acquisition by Workday enhances platform capabilities and customer base.

Partnership with Icertis could lead to innovative solutions and market expansion.

Recognition as a 'Top Performer' highlights strong market position and customer satisfaction.

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