LP
Kubernetes Engineer
LinkedIn Recruiter Post
Kubernetes Engineer role designing cloud - native infrastructure with Kubernetes and PaaS services. Focus on Azure cloud platforms, IaC tools, and DevOps practices.
About the role
- Senior Cloud Architect managing cloud-based AI/ML solutions at DoiT. Leading projects and advising customers in the AI/ML domain with a focus on AWS.
Responsibilities
- Lead the design and implementation of production-grade ML and Generative AI solutions on AWS (with awareness of multi-cloud environments).
- Act as a hands-on expert and trusted advisor for customers running AI/ML workloads at scale, from initial discovery through deployment and optimization.
- Translate complex business problems into cloud architectures that are secure, reliable, cost-efficient, and observable.
- Help evolve how DoiT uses AI/ML internally and with customers by turning one-off solutions into reusable patterns and "gravel roads" that influence the product roadmap.
- For Field Engineering, you will focus more on pre-sales, POVs, CloudBuild engagements, and partner-led growth motions.
- For Delivery, you will focus more on install base health, product adoption, proactive engagements, and account-team work.
- Own the technical success of your engagements: clearly define outcomes, make tradeoffs visible, and ensure designs are production-ready (security, reliability, performance, cost).
- Provide opinionated guidance on GenAI architectures (e.g., Amazon Bedrock, SageMaker, Q) and how they integrate with customers’ existing systems and processes.
- Coordinate with Account Managers, CSMs, TAMs, and other FDEs to ensure AI/ML engagements are sequenced correctly within broader account plans and install-base priorities.
Requirements
- 4+ years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads.
- Proven track record designing and operating large, distributed systems on AWS, selecting appropriate services and patterns to meet business and technical goals.
- Advanced proficiency with AWS services relevant to AI/ML and GenAI.
- Hands-on experience with Amazon Bedrock for deploying and scaling foundation models and Generative AI workloads.
- Experience fine-tuning and deploying Large Language Models (LLMs) and multimodal AI using Amazon SageMaker (including JumpStart).
- Strong prompt engineering skills and familiarity with rigorous model evaluation (quality, safety, performance).
- Understanding of agentic capabilities and patterns for AI agents that autonomously perform tasks and integrate with existing systems.
- Experience with Amazon Q Business and Amazon Q Developer (or similar tools) to accelerate insight generation and development workflows.
- In-depth knowledge of Amazon SageMaker components such as Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify for bias detection and interpretability.
- Proficiency integrating TensorFlow, PyTorch, and other ML frameworks with SageMaker for model development, fine-tuning, and deployment.
- Experience with distributed training (multi-GPU or multi-node) and performance optimization for inference.
- Strong data-engineering skills on AWS: Amazon S3, AWS Glue, Lake Formation, Redshift for AI/ML data pipelines.
- Experience building end-to-end AI/ML workflows using services like AWS Lambda, Step Functions, API Gateway, and containerized deployments on Amazon EKS / AWS Fargate.
- Hands-on experience with CI/CD for AI/ML using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar.
- Proficiency in monitoring and operating AI systems using Amazon CloudWatch and SageMaker Model Monitor.
- Strong understanding of AI governance, security, and compliance on AWS, including IAM, KMS, and data privacy patterns.
- Familiarity with AI ethics and bias detection/mitigation (e.g., using SageMaker Clarify or similar tools).
- Working knowledge of Google Cloud AI tools (e.g., Vertex AI, Cloud AutoML, BigQuery ML) sufficient to reason about multi-cloud architectures and integration points.
- Proven ability to mentor peers, run enablement sessions, and collaborate across Sales, CS, and Product.
- Excellent communication skills across technical and business audiences; able to simplify complex ideas and influence decisions.
- Natural ownership mentality: you escalate early, resolve fast, and own the outcome.
- Demonstrated ability to work effectively in a remote-first, global environment.
Benefits
- Unlimited Vacation
- Flexible Working Options
- Health Insurance
- Parental Leave
- Employee Stock Option Plan
- Home Office Allowance
- Professional Development Stipend
- Peer Recognition Program
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
Report this job
Found something wrong with the page? Please let us know by submitting a report below.
LP
Oracle Cloud Finance Consultant
LinkedIn Recruiter Post
Oracle Cloud Finance Consultant providing AMS support, period close, issue resolution, integrations, and team handling in Toronto. Requires 10+ years of Oracle Cloud experience.
LP
Senior Cloud Engineer (Google Cloud Platform + Terraform***)
LinkedIn Recruiter Post
Senior Cloud Engineer (GCP + Terraform) for a TOP5 Canadian bank. 12 - month contract, 3 - 4 days/week in Toronto, $80 - $90/hour.
LP
Cloud Engineer (GCP)
LinkedIn Recruiter Post
Cloud Engineer (GCP) role requiring 5+ years in infrastructure/DevOps/cloud, 3+ years with cloud platforms (1+ year GCP), and 3+ years Terraform experience. Hybrid contract position in Toronto.
LP
Oracle Cloud Finance Consultant
LinkedIn Recruiter Post
Oracle Cloud Finance Consultant with 10+ years experience in core modules. Contract role in Toronto, ON involving period close, issue resolution, and team management.
LP
AML Solution Architect with AWS and Snowflake
LinkedIn Recruiter Post
AML Solution Architect with AWS and Snowflake expertise. Contract role in Toronto requiring 8 years experience in solution architecture and AML systems.
LP
AWS Cloud Architect
LinkedIn Recruiter Post
AWS Cloud Architect role in Toronto, ON. Requires expertise in AWS, Terraform, Python, Docker/Kubernetes, EKS, microservices, hybrid cloud, and monitoring tools.
LP
Cloud Security Engineer
LinkedIn Recruiter Post
Cloud Security Engineer role focusing on Azure, M365, Zero Trust, IAM, endpoint security, and threat detection. Requires 5 years of cloud security experience.
LP
Azure Full Stack Developer
LinkedIn Recruiter Post
Azure Full Stack Developer contract role in Toronto, ON (hybrid). Requires 8 - 10 years experience in Azure cloud, DevOps, CI/CD, architecture, and security compliance.
AWS Developer contributing to large - scale cloud projects for digital transformation and infrastructure modernization across various sectors. Collaborating in a dynamic environment with emerging technologies.