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Full Stack Software Developer Contract Jobs in London, ON

Parsons

Senior Highway Engineer - MTO

London, ON · Hybrid

... your full potential. Unleash your talent and redefine what's possible. Overview: The Senior ... contract delivery, and oversight. * Advance knowledge of relevant software, such as AutoCAD ...

Parsons

Principal Highway Engineer - MTO

London, ON · On-site +1

CA$97K - CA$147K/yr

... full potential. Unleash your talent and redefine what's possible. The Principal Engineer in the ... contract delivery, and oversight. * Advance knowledge of relevant software, such as AutoCAD ...

New

DV Trading

Senior AI Engineer

London, ON

The long-term result is a firm that controls its own AI stack. Job Responsibilities: * Build and ... Requirements: * 5+ years software engineering; strong Python * Production fine-tuning or ...

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All JobsFull Stack Software Developer Contract JobsFull Stack Software Developer Contract Jobs in London, ON

Full Stack Software Developer Contract information

See London, ON salary details

$34.5K

$99.8K

$153.2K

How much do full stack software developer contract jobs pay per year?

As of Jul 24, 2026, the average yearly pay for full stack software developer contract in London, ON is $99,806.00, according to ZipRecruiter salary data. Most workers in this role earn between $78,979.00 and $118,685.00 per year, depending on experience, location, and employer.

Will Fullstack be replaced by AI?

Full Stack Software Developers perform a range of tasks that involve problem-solving, coding, and system design, which are difficult to fully automate with AI. While AI tools can assist with coding and testing, human expertise remains essential for complex development, architecture, and understanding user needs. Therefore, full stack roles are unlikely to be completely replaced by AI in the near future, but they will evolve with new tools and technologies.

What is a Full Stack Software Developer Contract job?

A Full Stack Software Developer Contract job is a temporary or project-based role where a developer is responsible for working on both the front-end and back-end of a software application. Contractors are typically hired for a set duration or until a project is completed and may work with multiple clients. They are expected to have expertise in various programming languages, frameworks, and databases to build and maintain complete web applications. This role often provides flexibility in work arrangements but requires strong problem-solving skills and adaptability to different tech stacks.

Are full-stack devs still in demand?

Full-stack software developers remain in high demand due to their versatility in handling both front-end and back-end development, with skills in frameworks like React and Node.js. Companies seek these developers for their ability to work across the entire technology stack, especially in agile environments and startups. Continuous learning of new tools and programming languages helps maintain their marketability.

What engineer makes $500,000 a year?

Highly experienced full stack software developers working in senior or specialized roles at large tech companies or startups can earn $500,000 or more annually, often including bonuses, stock options, and other compensation. Achieving this level typically requires advanced skills, extensive experience, and a strong track record in software development, architecture, or leadership positions.

How much do contract software developers make?

Contract software developers typically earn between $50 and $150 per hour, depending on experience, location, and project complexity. Senior developers with specialized skills or expertise in certain technologies can command higher rates, especially for short-term or high-demand projects.

What are some typical challenges faced by Full Stack Software Developers in contract roles?

Full Stack Software Developers working on contract often face the challenge of quickly adapting to new codebases and team workflows, as projects may vary widely between clients. They may need to work independently while also collaborating with in-house developers, designers, and project managers to align on technical and business requirements. Managing tight deadlines, prioritizing tasks, and learning unfamiliar technologies or systems on the fly are common aspects of the role. Adapting quickly and maintaining clear communication are key to ensuring successful project delivery and ongoing client satisfaction.

What are the key skills and qualifications needed to thrive in the Full Stack Software Developer Contract position, and why are they important?

A Full Stack Software Developer on contract needs proficiency in both front-end and back-end technologies, such as JavaScript frameworks, server-side languages, and databases, along with a solid understanding of software development principles. Familiarity with tools like Git, containerization platforms (e.g., Docker), CI/CD pipelines, and cloud services, as well as relevant certifications, is often expected. Excellent problem-solving abilities, adaptability, and strong communication skills are essential for efficiently collaborating in dynamic environments and managing multiple project requirements. These skills enable success in delivering high-quality, scalable solutions within set deadlines while meeting client or organizational needs.

What are the most commonly searched types of Full Stack Software Developer jobs in London, ON? The most popular types of Full Stack Software Developer jobs in London, ON are:
What are popular job titles related to Full Stack Software Developer Contract jobs in London, ON? For Full Stack Software Developer Contract jobs in London, ON, the most frequently searched job titles are:
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Full Stack Software Developer Contract jobs near you
Senior Software Engineer - Dev Ops

Senior Software Engineer - Dev Ops

CARFAX

London, ON

Full-time

Medical, Retirement

Posted 27 days ago

Job description

Join Team CARFAX as a Senior Software Engineer - Dev Ops
 
We are looking for a seasoned Senior Software Engineer - Dev Ops to join our platform team and take an active role in designing, scaling, and operating the infrastructure that powers Large Language Model (LLM) development and hosting. This is a high-impact, highly technical position where you will own critical platform components, drive architectural decisions, and directly shape the reliability, performance, and security of our AI infrastructure.
 
At its core, this is a Kubernetes-first, cloud-native platform engineering role. We care deeply about your ability to architect and operate scalable, resilient infrastructure for LLM workloads — the specific cloud or tooling you've built that experience on is secondary. Our current platform runs on AWS with EKS, Flyte, ArgoCD, JupyterHub, and the LGTM observability stack, and you'll be working within that environment — but we are far more interested in the depth of your platform thinking than in a specific vendor background.
 
If you are an engineer who thrives at the intersection of AI/ML and cloud-native infrastructure, who gets excited about solving the unique scaling and operational challenges that LLM workloads demand, and who wants to work on technology that sits at the absolute cutting edge of the AI industry — this role was built for you.
 
At CARFAX, we believe in the power of teamwork and value in-person interactions so that we can collaborate and thrive together. This position will require 2 days in the London, ON office per week, subject to change with future business needs. One last thing: Our four-day week continues in Summer 2026! 
 
What You'll Own:
 
LLM Platform Architecture — Actively participate in the design and evolution of the core infrastructure platform supporting LLM training, fine-tuning, and inference workloads at scale, contributing architectural decisions that balance performance, cost, and reliability across the full platform lifecycle.
 
Kubernetes & Advanced Autoscaling — Own the design and implementation of sophisticated K8s autoscaling strategies (HPA, VPA, KEDA, Cluster Autoscaler) tailored to the highly variable and GPU-intensive demands of LLM workloads. Our current environment is EKS, though equivalent production Kubernetes experience on GKE, AKS, or on-prem is equally valued.
 
ML Workflow Orchestration — Participate in the engineering and optimization of ML pipeline infrastructure, contributing to best practices for pipeline design, resource allocation, and workflow reliability across LLM training and evaluation workloads. We currently use Flyte — experience with comparable platforms such as Kubeflow, Airflow, or Prefect translates well.
 
AI Developer Platform — Own and contribute to the architecture and operations of interactive compute environments used by AI researchers and LLM engineers to develop, experiment, and prototype. We run JupyterHub today, though experience with equivalent multi-user ML development platforms is directly applicable.
 
CI/CD & GitOps — Participate in the development and ongoing improvement of GitOps workflows and CI/CD pipelines, contributing to deployment best practices and enabling rapid, reliable delivery of platform changes. Our current implementation uses ArgoCD — strong experience with GitOps principles and comparable tooling is what matters.
 
Observability & Reliability — Contribute to the full observability stack implementation — designing dashboards, defining SLOs, building alerting frameworks, and ensuring deep visibility into LLM workload performance and platform health. We use the LGTM stack (Loki, Grafana, Tempo, Mimir) — experience with Prometheus, OpenTelemetry, ELK, Datadog, or equivalent platforms is welcomed.
 
Cloud Infrastructure — Participate in cloud infrastructure design across compute (including GPU instance families), storage, networking, and IAM, with a strong emphasis on cost optimization and operational excellence. Our primary cloud is AWS — candidates with strong GCP or Azure backgrounds who are prepared to work in AWS are encouraged to apply.
 
Security & Compliance — Engage actively in the vulnerability assessment and remediation program across all platform components, contributing to security standards and ensuring the LLM platform meets organizational and regulatory compliance requirements.
 
Collaborative Engineering — Participate in technical design reviews, contribute to roadmap discussions, and serve as a knowledgeable resource and collaborative partner across AIOps and MLOps disciplines
 
Required Experience & Skills:
 
7+ years of experience in DevOps, Platform Engineering, MLOps, or a closely related infrastructure discipline.
 
Deep Kubernetes expertise — production experience operating Kubernetes at scale on any major managed platform (EKS, GKE, AKS) or on-premises, with advanced knowledge of scheduling, autoscaling, networking, RBAC, and cluster operations.
 
Cloud infrastructure proficiency — extensive experience designing and operating production workloads on at least one major cloud provider (AWS, GCP, or Azure), covering compute, storage, networking, and identity and access management
 
MLOps / AI Infrastructure experience — demonstrated experience building and operating infrastructure that supports ML training, model serving, or LLM workloads, including GPU resource management and scheduling at scale
 
CI/CD & GitOps — strong hands-on experience with GitOps principles and modern CI/CD pipeline design, using any mainstream tooling (ArgoCD, Flux, GitHub Actions, Tekton, or equivalent)
 
Observability Engineering — production experience designing and operating observability platforms including metrics, logging, and distributed tracing, using any modern stack (Grafana/LGTM, Prometheus, Datadog, ELK, or equivalent)
 
Infrastructure as Code — strong proficiency with Terraform, Helm, or comparable IaC and configuration management tooling.
 
Programming & Scripting — solid coding ability in Python and/or Go, with experience writing automation, tooling, and infrastructure integrations.
 
Security Mindset — hands-on experience with vulnerability scanning, remediation workflows, and cloud security best practices including RBAC hardening and secrets management
 
Strongly Preferred:
Direct experience with Flyte or comparable ML workflow orchestration platforms (Kubeflow, Airflow, Prefect, Metaflow)
Experience operating JupyterHub or equivalent multi-user interactive compute platforms at scale
Familiarity with LLM-specific infrastructure — model serving frameworks (vLLM, Triton, TorchServe), GPU cluster management, large-scale distributed training setups
Hands-on experience with AWS (EKS, EC2 GPU families, S3, IAM, VPC) as our current primary cloud environment
Experience with FinOps practices — cloud cost attribution, rightsizing, and spot/preemptible instance strategies for ML workloads
Relevant certifications: CKA / CKS, AWS/GCP/Azure Solutions Architect or DevOps Engineer, or equivalent
Who You Are:
A systems thinker who understands how architectural decisions ripple across reliability, performance, cost, and security — regardless of which cloud or tooling stack those decisions are made within
Operationally minded — you build things to be observable, maintainable, and resilient from day one
Deeply curious about AI and LLMs — you understand why the infrastructure you build matters and stay current with how the AI landscape is evolving
Proactive and ownership-driven — you identify problems before they become incidents and drive solutions to completion
An effective collaborator and communicator who can translate complex infrastructure concepts for AI researchers, data scientists, and engineering leadership alike
Comfortable operating with autonomy in a fast-moving environment where priorities evolve alongside the AI landscape
 
Why This Role Stands Out:
LLM infrastructure is one of the most technically demanding and strategically important engineering domains in the industry today. As a senior member of our AIOps team you will:
Directly shape the platform that enables LLM development and productionization — your contributions will have immediate, measurable impact
Work on genuinely hard infrastructure problems — GPU scheduling, large-scale distributed workloads, high-throughput model serving, and multi-tenant ML environments
Be positioned at the epicenter of the AI infrastructure space, one of the fastest growing and highest-demand engineering disciplines in the industry
Have a clear voice in technical direction — your experience and opinions on platform design are genuinely valued and actively sought
Bring your full experience to the table — whether you've built on AWS, GCP, Azure, or hybrid environments, your platform engineering expertise is what drives impact here
 
What’s in it for you: 
  • Competitive Compensation: Attractive salary, comprehensive benefits, and generous time-off policies. 
  • Flexible Work Schedules: Enjoy 4-day summer work weeks and a winter holiday break. 
  • Retirement Support: 401(k) / DCPP matching. 
  • Performance Rewards: Annual bonus program to recognize your contributions. 
  • Innovative Workspace: Casual, dog-friendly offices designed for creativity and collaboration. 
 
Hear from our Team: Our accolades speak for themselves:
  • 10X Virginia Business Best Places to Work
  • 9X Washingtonian Great Places to Work
  • 9X Washington Post Top Workplace
  • St. Louis Post-Dispatch Best Places to Work
Vacancy Status:
This posting is for an existing vacancy.
 
Base Salary:
The anticipated base salary range for this position is CAD $92,500 to $136,000 annually. Final base salary will be determined based on geographical location, experience, and qualifications.
 
Benefits:
Join a company that values your total wellbeing. Carfax offers competitive compensation, comprehensive healthcare coverage, and the chance to make a meaningful impact in an industry-leading organization.  Our benefit offerings can be found at: CARFAX Careers.