26 days ago
Remote, IrelandSenior
Responsibilities
- Lead the design and implementation of production-grade ML and Generative AI solutions on AWS.
- Advise customers on AI/ML workloads, cloud architecture, cost efficiency, reliability, security, performance, and observability.
- Deliver cloud optimization sessions, performance workshops, security and reliability reviews, and architecture assessments.
- Deploy and operate ML/GenAI workloads, including training, inference, GPU utilization, scaling, MLOps, 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, features, agent skills, scripts, and internal tooling to DoiT Cloud Intelligence.
- Work as an embedded technical partner with Customer Success Managers, Account Managers, customers, peer FDEs, and internal product teams.
- Use DoiT Cloud Intelligence products to build dashboards, identify optimization opportunities, create insights, automate workflows, and support governance processes.
- Mentor peers, run enablement sessions, document architectures and decisions, and communicate technical topics to technical and business stakeholders.
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 AI/ML services and hands-on experience with Amazon Bedrock.
- Experience fine-tuning and deploying LLMs and multimodal AI using Amazon SageMaker, including JumpStart.
- Strong prompt engineering, model evaluation, agentic AI, and AI governance knowledge.
- Experience with SageMaker Pipelines, Model Monitor, Data Wrangler, SageMaker Clarify, TensorFlow, and PyTorch.
- Experience with distributed training, inference optimization, AWS data engineering, and end-to-end AI/ML workflows.
- Experience with AWS Lambda, Step Functions, API Gateway, Amazon EKS, AWS Fargate, AWS CodePipeline, CodeBuild, and Amazon CloudWatch.
- Understanding of AWS security and compliance patterns involving IAM, KMS, data privacy, bias detection, and mitigation.
- Working knowledge of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and BigQuery ML.
- Ability to mentor peers, collaborate across Sales, Customer Success, and Product, and communicate with technical and business audiences.
- A BA/BS in Computer Science, Mathematics, or a related technical field is preferred, or equivalent practical experience.
- Preferred qualifications include RLHF, advanced fine-tuning, hybrid AI architectures, Hugging Face, AI/ML certifications, consulting or SaaS experience, and JIRA.
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.
- 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 and contractors, with a global and remote working environment.
Tech Stack
Categories
Forward Deployed
