Azure Cloud Engineer AI

Job Description

Themesoft Inc. is a global IT solutions provider and a Woman Owned Minority Business Enterprise headquartered in Dallas, TX. With a strong presence across the US, Canada, India, Singapore, and Brazil, we specialize in digital transformation, consulting, and workforce solutions across diverse industries.

We are currently looking for a tech-savvy and results-driven professional for one of our leading clients. If you're passionate about technology and looking to grow in a dynamic, fast-paced environment, this could be the perfect fit for you!

Role : Azure Cloud Engineer AI

Location : Toronto, Canada- Hybrid (3 days to office)

6+ months

Cloud Engineer - AI Infrastructure

Role Overview

As a Cloud Engineer, you will be responsible for implementing and maintaining scalable, secure, and high-performance cloud infrastructure to support AI/ML workloads. You'll work closely with platform, application, and data teams to ensure reliable operations and efficient delivery of AI services.

Key Responsibilities

Infrastructure & Platform Operations

• Deploy and manage cloud-native infrastructure for AI/ML workloads (GPU/CPU clusters, autoscaling, spot instances).
• Configure and maintain networking components (Azure VNet, Private Link, peering, HA/DR setups).
• Operate storage and database systems including Azure Data Lake Storage, relational databases, and vector databases (FAISS, Milvus, Pinecone).
• Implement IAM policies, secrets management (Key Vault), and encryption standards.

Observability & Reliability

• Set up monitoring for latency, throughput, GPU utilization, and cost metrics.
• Integrate logging and tracing tools (OpenTelemetry) and maintain SLOs/SLIs for infrastructure services.
• Support incident response and root cause analysis using SRE principles.

CI/CD & Infrastructure Automation

• Build and maintain CI/CD pipelines using GitHub Actions or Azure DevOps.
• Implement GitOps workflows for infrastructure-as-code using Terraform or Bicep.
• Create reusable IaC modules and templates for consistent deployments.

FinOps & Cost Optimization

• Monitor and optimize GPU usage, caching strategies, and inference performance.
• Support cost governance and reporting for AI infrastructure.

Application Enablement

• Provide infrastructure support for APIs, microservices, and event-driven architectures.
• Enable model serving runtimes (TensorRT-LLM, vLLM, Triton/KServe).
• Support RAG pipelines including embeddings, chunking, and retrieval systems.

Security & Compliance

• Apply defense-in-depth strategies: IAM least privilege, private networking, image signing.
• Ensure compliance with data residency, encryption, and audit requirements.

Qualifications

• Bachelor's degree in Computer Science, Engineering, or related field.
• 3-5 years of experience in cloud infrastructure (Azure preferred).
• Hands-on experience with Kubernetes, Terraform/Bicep, and cloud networking.
• Familiarity with AI/ML infrastructure components and model serving.
• Proficiency in Python for automation; Go or TypeScript is a plus.

Tech Stack

• Cloud & Infra: Azure (AKS, Functions, Event Hubs, Key Vault), Terraform/Bicep, GitHub Actions
• AI Infra: Kubernetes, KServe/Triton, vLLM, TensorRT-LLM
• Ops: Prometheus, Grafana, OpenTelemetry, ArgoCD
• Data: Feature stores (Feast), vector DBs (FAISS, Milvus), relational DBs
• App Layer: APIs, microservices, frontend/backend integration

Success Metrics

• Reliability: SLOs met, uptime maintained
• Security: No critical vulnerabilities, audit-ready infrastructure
• Cost Efficiency: Optimized GPU and infra spend
• Velocity: Fast and reliable deployments
• Collaboration: Effective cross-team support and documentation

Regards,

_

Parthasarathy K

Lead Recruiter

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