26 days ago
Remote, EMEASenior
Responsibilities
- Design and implement production-grade ML and Generative AI solutions on AWS, with awareness of multi-cloud architectures.
- Architect secure, reliable, observable, cost-efficient, and scalable cloud systems for customer AI/ML workloads.
- Deliver cloud optimization, performance, security, reliability, and well-architected assessments and workshops.
- Deploy and operate ML/GenAI training and inference workloads, including GPU utilization, scaling, MLOps, monitoring, logging, and FinOps.
- Create reusable playbooks, Terraform modules, CloudFlow templates, cloud diagrams, Composer recipes, documentation, scripts, and internal tools.
- Provide product feedback and contribute code or features to DoiT Cloud Intelligence based on customer usage.
- Work as the technical partner within account teams alongside Customer Success Managers, Account Managers, customers, and peer FDEs.
- Use DoiT Cloud Intelligence products to build analytics, allocations, insights, automations, recipes, dashboards, and reports.
- Mentor peers, lead enablement sessions, document technical decisions, and communicate complex topics to technical and non-technical stakeholders.
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, SageMaker JumpStart, large language models, multimodal AI, prompt engineering, and model evaluation.
- Understanding of AI agents, agentic capabilities, Amazon Q Business, and Amazon Q Developer or similar tools.
- Knowledge of SageMaker Pipelines, Model Monitor, Data Wrangler, SageMaker Clarify, distributed training, and inference optimization.
- Experience integrating TensorFlow and PyTorch with SageMaker for model development, fine-tuning, and deployment.
- Strong AWS data-engineering experience with Amazon S3, AWS Glue, Lake Formation, and Redshift.
- Experience building AI/ML workflows with AWS Lambda, Step Functions, API Gateway, Amazon EKS, and AWS Fargate.
- Experience with CI/CD for AI/ML, monitoring, AI governance, security, compliance, IAM, KMS, data privacy, and bias 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.
- A BA/BS in Computer Science, Mathematics, or a related technical field, or equivalent practical experience, is listed as a bonus qualification.
- Additional data or AI certifications, RLHF, advanced fine-tuning, hybrid AI architectures, Hugging Face, consulting or SaaS experience, JIRA, and Agile exposure are bonus qualifications.
Benefits
- Remote-first work with flexible working options across the listed eligible countries and regions.
- 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 in Eastern Europe or Portugal.
- The company supports professional and personal skill development and a globally distributed work environment.
Tech Stack
Categories
Forward Deployed
