26 days ago
Remote, EMEASenior
Responsibilities
- Lead the design and implementation of production-grade ML and Generative AI solutions on AWS.
- Advise customers on cloud cost efficiency, reliability, resilience, security, performance, automation, and operational excellence.
- Deliver cloud optimization sessions, architecture deep dives, security posture reviews, reliability reviews, and technical workshops.
- Respond to complex cloud engineering inquiries and provide high-quality technical resolutions.
- Deploy and operate ML/GenAI workloads, including training, inference, GPU utilization, scaling, monitoring, logging, and FinOps.
- Convert customer solutions into reusable playbooks, Terraform modules, CloudFlow templates, diagrams, recipes, and documentation.
- Provide product and engineering feedback and contribute code or ownership to DoiT Cloud Intelligence features.
- Build agent skills, scripts, and internal tooling and support enablement through documentation, demos, office hours, and training.
- Partner with Customer Success Managers, Account Managers, customers, and peer FDEs on technical deployments, integrations, automation, and cloud optimization.
- Use DoiT Cloud Intelligence products to create dashboards, identify opportunities, build insights, automate workflows, and support cloud operations.
Requirements
- At least 4 years of experience architecting, deploying, and managing cloud-based AI/ML solutions, including production workloads.
- 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, foundation models, LLMs, multimodal AI, prompt engineering, model evaluation, and agentic AI patterns.
- Knowledge of SageMaker Pipelines, Model Monitor, Data Wrangler, and SageMaker Clarify.
- Experience with TensorFlow, PyTorch, distributed training, inference optimization, and end-to-end ML workflows.
- Strong AWS data-engineering experience with Amazon S3, AWS Glue, Lake Formation, and Redshift.
- Experience with AWS Lambda, Step Functions, API Gateway, Amazon EKS, and AWS Fargate for AI/ML workflows and containerized deployments.
- Experience with AI/ML CI/CD using AWS CodePipeline, AWS CodeBuild, SageMaker Pipelines, or similar tools.
- Knowledge of Amazon CloudWatch, IAM, KMS, AI governance, security, compliance, data privacy, and bias detection or mitigation.
- Working knowledge of Google Cloud AI tools, including Vertex AI, Cloud AutoML, and BigQuery ML.
- Ability to mentor peers, run enablement sessions, collaborate across Sales, Customer Success, and Product, and communicate with technical and business audiences.
- BA/BS in Computer Science, Mathematics, or a related technical field, or equivalent practical experience is preferred.
- Additional data or AI certifications, RLHF, advanced fine-tuning, hybrid AI architectures, Hugging Face, JIRA, or experience as an ML Engineer, Data Scientist, or AI-focused Architect are bonuses.
Benefits
- Remote-first work with flexible working options and schedules.
- Full-time employee benefits include unlimited vacation, health insurance, parental leave, an employee stock option plan, home office allowance, professional development stipend, and peer recognition program.
- The role is available to full-time employees in the UK, Ireland, Estonia, Sweden, the Netherlands, and Israel, and to contractors in Eastern Europe or Portugal.
Tech Stack
Categories
Forward Deployed
