Python Developer

Acestack; Toronto; On-site; CAD 90,000 - 120,000; Full time

Job summary

Acestack is seeking a Python Developer in Toronto, ON, to design, build, and optimize scalable data services for AI and ML. The role emphasizes distributed systems, cloud and on‑prem deployments, and close collaboration with infra and ML teams.

You will develop data services, ensure code quality and performance, and implement observability and automated testing across hybrid environments including AWS, Azure, and on‑premises.

Qualifications

• Experience designing distributed data service architectures.
• Strong software engineering practices and quality standards.
• Hands-on with scalable applications and services.
• Experience deploying across hybrid cloud and on‑prem environments.

Responsibilities

• Design, build, and optimize data services that support AI and ML applications.
• Architect and implement software engineering standards for code quality, performance, scalability, and maintainability.
• Collaborate with infrastructure engineers and ML researchers for reliable data applications.
• Build scalable data automation solutions across cloud and on‑prem environments.
• Design and develop distributed systems and software architectures for data services.
• Apply best practices including code reviews, testing, design patterns, and architecture.
• Build and deploy scalable applications and services using modern frameworks.
• Implement observability, monitoring, and logging for visibility into performance.
• Support deployments across hybrid environments including AWS, Azure, and on‑premises.

Skills

Distributed Systems

Docker & Kubernetes

Python

Apache Spark

SQL

MongoDB

Observability

Collaboration

Tools

OpenShift

Job description

Python Developer

Job Type: Full Time
Location: Toronto, ON
Work Model: Onsite / 4 Days

Primary Skills: Python & Apache Spark, Distributed Systems, Docker/Kubernetes & Cloud

Job Description

We are seeking an experienced Python Developer to design, build, and optimize scalable data services supporting AI and ML applications. The role involves developing distributed systems, cloud and on-premises data solutions, and highly resilient applications while collaborating closely with infrastructure engineers and machine learning teams.

Roles & Responsibilities

• Design, build, and optimize data services that support AI and ML applications.
• Architect and implement software engineering standards for code quality, performance, scalability, and maintainability.
• Collaborate with infrastructure engineers and machine learning researchers to ensure reliable and scalable data applications.
• Build highly scalable and resilient data automation solutions across cloud and on-premises environments.
• Design and develop distributed systems and software architectures for data services.
• Apply software engineering best practices including code reviews, automated testing, design patterns, and architectural standards.
• Build and deploy scalable applications and services using modern frameworks and technologies.
• Implement observability, monitoring, and logging solutions to provide visibility into application behavior and performance.
• Support application deployments across hybrid environments including AWS, Azure, and on-premises infrastructure.

Must Have Skills & Experience

• Strong experience designing and implementing distributed systems and data service architectures.
• Proven expertise in software engineering practices, including code reviews, testing, design patterns, and software architecture.
• Hands-on experience building and deploying scalable applications and services.
• Strong knowledge of Docker and Kubernetes or OpenShift Container Platform (OCP4).
• Experience deploying applications across hybrid environments, including on-premises and major cloud platforms such as AWS and Azure.
• Medium to strong proficiency in Python, SQL, and Apache Spark.
• Experience designing data access layers and working with SQL and NoSQL databases, including MongoDB.
• Experience with observability, monitoring, and logging practices.
• Strong problem-solving and analytical skills with the ability to work collaboratively across engineering and data science teams.

Preferred Qualifications

• Experience supporting AI/ML application platforms.
• Knowledge of cloud-native architecture and containerized data services.
• Experience with highly available and fault-tolerant distributed applications.
• Experience working with large-scale data processing and automation.
• Financial services or enterprise technology experience is an advantage.

Job Details

• Job Type: Full Time
• Location: Toronto, ON
• Work Model: Onsite / 4 Days