26 days ago
Remote, CanadaSenior
Responsibilities
- Lead the design and implementation of production-grade ML and Generative AI solutions on AWS with awareness of multi-cloud environments.
- Advise customers on scalable AI/ML workloads, cloud architecture, cost efficiency, reliability, security, performance, and operational optimization.
- Deliver cloud optimization sessions, workshops, security and reliability reviews, architecture deep dives, and complex technical support engagements.
- Develop reusable playbooks, Terraform modules, CloudFlow templates, cloud diagrams, Composer Recipes, documentation, agent skills, scripts, and internal tooling.
- Provide product feedback and contribute code or features to DoiT Cloud Intelligence where appropriate.
- Operate as the technical partner within account teams alongside Customer Success Managers and Account Managers.
- Use DoiT Cloud Intelligence products to create dashboards, identify opportunities, build insights, automate workflows, and support cloud operations.
- Collaborate with customer engineers, architects, FinOps teams, peer FDEs, Product, Engineering, Sales, and Customer Success.
- Document architectures and decisions, run enablement sessions, mentor peers, and share technical learnings.
Requirements
- At least 4 years of experience architecting, deploying, and managing production cloud-based AI/ML solutions.
- Proven experience designing and operating large distributed systems on AWS.
- Advanced proficiency with AWS services relevant to AI/ML and Generative AI.
- Hands-on experience with Amazon Bedrock, Amazon SageMaker, SageMaker JumpStart, LLMs, multimodal AI, prompt engineering, model evaluation, and agentic AI patterns.
- Knowledge of SageMaker Pipelines, Model Monitor, Data Wrangler, SageMaker Clarify, TensorFlow, PyTorch, distributed training, and inference optimization.
- Strong AWS data-engineering experience with Amazon S3, AWS Glue, Lake Formation, and Redshift.
- Experience building workflows with AWS Lambda, Step Functions, API Gateway, Amazon EKS, and AWS Fargate.
- Experience with AI/ML CI/CD using AWS CodePipeline, CodeBuild, SageMaker Pipelines, or similar tools.
- Knowledge of monitoring, AI governance, security, compliance, IAM, KMS, data privacy, and bias detection or mitigation.
- Working knowledge of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and BigQuery ML.
- Ability to mentor peers, run enablement sessions, communicate with technical and business audiences, and collaborate in a remote global environment.
- A BA/BS in Computer Science, Mathematics, or a related technical field, or equivalent practical experience, is a bonus qualification.
- Additional data or AI certifications, RLHF, advanced fine-tuning, hybrid AI architecture, Hugging Face, consulting or SaaS experience, and JIRA experience are preferred.
Benefits
- Remote-first full-time employment in Canada, preferably aligned with Eastern Time.
- Unlimited vacation and flexible working options.
- Health insurance and parental leave.
- Employee Stock Option Plan.
- Home office allowance and professional development stipend.
- Peer recognition program.
