Senior Llmops Engineer -Cloud / Ai Infrastructure
Talent To Hire Inc.
Location:
Downtown TorontoHybrid:
4 days in office
Ready to build what powers the next generation of AI?
We’re looking for a
Staff LLMOps Engineer
to lead the design, deployment, and optimization of
large language model (LLM)
infrastructure on the cloud.You’ll be the driving force behind taking trained models from lab to production—scaling efficiently across multi-GPU clusters and pushing the boundaries of
inference performance
for enterprise-grade AI applications.
If you thrive at the intersection of
AI, cloud engineering, and systems optimization, this is your chance to shape the future of large-scale model serving in a high-impact environment.
What You’ll DoArchitect and operationalize
LLM deployment pipelines
on AWS and Kubernetes/EKS.
Build and scale
multi-GPU inference infrastructure
for low latency, high availability, and cost efficiency.
Optimize inference using frameworks like
vLLM, SGLang, and DeepSpeed-Inference .
Implement advanced serving techniques: continuous batching, speculative decoding, KV-cache management, and distributed scheduling.
Collaborate with AI researchers to convert model training outputs into
production-grade APIs and services .
Establish
observability and monitoring
for latency, throughput, GPU utilization, and failure recovery.
Automate provisioning, scaling, and upgrades using
Terraform and CI/CD pipelines .
Ensure compliance, security, and efficiency in multi-tenant LLM hosting for enterprise clients.
What We’re Looking For6+ years
in DevOps, ML infrastructure, or cloud platform engineering.
2+ years
of direct experience deploying and optimizing LLMs or large-scale ML models.
Expertise with
GPU-accelerated inference
and distributed serving environments.
Deep familiarity with
cloud-native architectures (AWS, GCP, Azure)
and
Kubernetes .
Strong foundation in
Python, Bash, and IaC (Terraform) .
Experience integrating
monitoring tools
(Prometheus, Grafana, Datadog) for performance visibility.
Passion for building robust, scalable, and secure AI systems.
Why JoinLead and
own mission-critical AI infrastructure
at a fast-scaling startup.
Work alongside world-class engineers, data scientists, and innovators.
Competitive salary +
meaningful equity
in a company redefining applied AI.
A culture built on innovation, technical depth, and impact—your work truly matters.
#J-18808-Ljbffr
Downtown TorontoHybrid:
4 days in office
Ready to build what powers the next generation of AI?
We’re looking for a
Staff LLMOps Engineer
to lead the design, deployment, and optimization of
large language model (LLM)
infrastructure on the cloud.You’ll be the driving force behind taking trained models from lab to production—scaling efficiently across multi-GPU clusters and pushing the boundaries of
inference performance
for enterprise-grade AI applications.
If you thrive at the intersection of
AI, cloud engineering, and systems optimization, this is your chance to shape the future of large-scale model serving in a high-impact environment.
What You’ll DoArchitect and operationalize
LLM deployment pipelines
on AWS and Kubernetes/EKS.
Build and scale
multi-GPU inference infrastructure
for low latency, high availability, and cost efficiency.
Optimize inference using frameworks like
vLLM, SGLang, and DeepSpeed-Inference .
Implement advanced serving techniques: continuous batching, speculative decoding, KV-cache management, and distributed scheduling.
Collaborate with AI researchers to convert model training outputs into
production-grade APIs and services .
Establish
observability and monitoring
for latency, throughput, GPU utilization, and failure recovery.
Automate provisioning, scaling, and upgrades using
Terraform and CI/CD pipelines .
Ensure compliance, security, and efficiency in multi-tenant LLM hosting for enterprise clients.
What We’re Looking For6+ years
in DevOps, ML infrastructure, or cloud platform engineering.
2+ years
of direct experience deploying and optimizing LLMs or large-scale ML models.
Expertise with
GPU-accelerated inference
and distributed serving environments.
Deep familiarity with
cloud-native architectures (AWS, GCP, Azure)
and
Kubernetes .
Strong foundation in
Python, Bash, and IaC (Terraform) .
Experience integrating
monitoring tools
(Prometheus, Grafana, Datadog) for performance visibility.
Passion for building robust, scalable, and secure AI systems.
Why JoinLead and
own mission-critical AI infrastructure
at a fast-scaling startup.
Work alongside world-class engineers, data scientists, and innovators.
Competitive salary +
meaningful equity
in a company redefining applied AI.
A culture built on innovation, technical depth, and impact—your work truly matters.
#J-18808-Ljbffr
Vacancy posted 1 hour ago
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