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.
Nice to Have
- BA/BS degree in Computer Science, Mathematics, or a related technical field, or equivalent practical experience.
- Additional data or AI certifications (e.g., AWS/GCP data certifications, reputable AI/ML programs such as Stanford, Coursera, Udacity, MIT, eCornell).
- Expanded AI/ML & Dev Experience Experience with modern RLHF, advanced fine-tuning techniques, and hybrid AI architectures.
- Familiarity with Hugging Face or similar open-source ecosystems integrated with AWS.
- Prior experience as a ML Engineer, Data Scientist, or AI-focused Architect in a consulting or SaaS environment.
- Experience with JIRA or similar tools for tracking work across delivery and product-feedback cycles.
- Exposure to Agile practices and frameworks commonly used for SaaS and cloud delivery.
Work Arrangement
Remote (Worldwide) — Canada, United States, Worldwide
Additional Information
- Unlimited Vacation
- Flexible Working Options
- Health Insurance
- Parental Leave
- Employee Stock Option Plan
- Home Office Allowance
- Professional Development Stipend
- Peer Recognition Program
