26 days ago
Remote, SwedenSenior
Responsibilities
- Design and implement production-grade ML and Generative AI solutions on AWS, with awareness of multi-cloud architectures.
- Serve as a hands-on technical advisor for customers running AI/ML workloads at scale from discovery through deployment and optimization.
- Design solutions for cloud cost efficiency, reliability, resilience, security, performance, automation, observability, and operational governance.
- Deliver cloud optimization sessions, cost and performance workshops, security and reliability reviews, and architecture deep dives.
- Respond to complex cloud engineering support requests and provide clear, high-quality resolutions.
- Convert customer solutions into reusable playbooks, Terraform modules, CloudFlow templates, diagrams, recipes, and documentation.
- Provide product and engineering feedback, contribute code or DCI features, and build agent skills, scripts, and internal tooling.
- Work within account teams alongside Customer Success Managers and Account Managers to drive technical deployment, integration, adoption, and customer health.
- Use DoiT Cloud Intelligence products to build analytics and allocation dashboards, identify opportunities, create Composer recipes, and implement CloudFlow automations.
- Mentor peers, run enablement sessions, document architectures and decisions, and collaborate with customers and internal teams.
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 AWS AI/ML and GenAI expertise, including hands-on Amazon Bedrock experience.
- Experience fine-tuning and deploying LLMs and multimodal AI with Amazon SageMaker and JumpStart.
- Strong prompt engineering, model evaluation, agentic AI, and Amazon Q Business or Amazon Q Developer experience.
- Knowledge of SageMaker Pipelines, Model Monitor, Data Wrangler, Clarify, distributed training, and inference optimization.
- Experience integrating TensorFlow and PyTorch with SageMaker.
- Strong AWS data engineering experience using S3, Glue, Lake Formation, and Redshift.
- Experience building AI/ML workflows with Lambda, Step Functions, API Gateway, EKS, and Fargate.
- Experience with AI/ML CI/CD, CloudWatch monitoring, IAM, KMS, governance, security, compliance, and data privacy.
- Working knowledge of Google Cloud AI tools including Vertex AI, Cloud AutoML, and BigQuery ML.
- Ability to mentor peers, communicate with technical and business audiences, and work effectively in a remote global environment.
- BA/BS in Computer Science, Mathematics, or a related technical field, or equivalent practical experience is preferred.
- Preferred experience includes RLHF, advanced fine-tuning, hybrid AI architectures, Hugging Face, AI/ML consulting or SaaS work, JIRA, and Agile delivery practices.
Benefits
- Remote-first work with flexible working options across the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel; contractor opportunities are available in Eastern Europe or Portugal.
- Unlimited vacation.
- Health insurance.
- Parental leave.
- Employee stock option plan.
- Home office allowance.
- Professional development stipend.
- Peer recognition program.
Tech Stack
Categories
Forward Deployed
