26 days ago
Remote, AmericasSenior
Responsibilities
- Lead the design and implementation of production-grade ML and Generative AI solutions on AWS with awareness of multi-cloud architectures.
- Advise customers on cloud cost efficiency, reliability, resilience, security posture, performance, automation, and operational toil reduction.
- Deliver cloud optimization sessions, performance workshops, security and reliability reviews, architecture deep dives, and expert inquiry resolutions.
- Deploy and operate ML/GenAI workloads, including training, inference, GPU utilization, scaling, monitoring, logging, and FinOps.
- Convert one-off customer solutions into reusable patterns, Terraform modules, playbooks, CloudFlow templates, diagrams, recipes, and documentation.
- Provide product feedback and contribute code, features, agent skills, scripts, and internal tooling to DoiT Cloud Intelligence.
- Work with Customer Success Managers, Account Managers, customer engineers, architects, and FinOps teams to execute technical optimization plans.
- Use DoiT Cloud Intelligence products to build dashboards, reports, insights, automations, integrations, and governance workflows.
- Mentor peers, run enablement sessions, document architectures and decisions, and communicate technical concepts to technical and business audiences.
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 expertise relevant to AI/ML and Generative AI, including Amazon Bedrock and Amazon SageMaker.
- Experience fine-tuning and deploying LLMs and multimodal AI, with prompt engineering and model evaluation skills.
- Understanding of AI agents, agentic patterns, Amazon Q Business, and Amazon Q Developer or similar tools.
- Knowledge of SageMaker Pipelines, Model Monitor, Data Wrangler, SageMaker Clarify, TensorFlow, PyTorch, distributed training, and inference optimization.
- 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 AI/ML CI/CD, monitoring, governance, security, compliance, IAM, KMS, data privacy, and bias mitigation.
- Working knowledge of Google Cloud AI tools such as Vertex AI, Cloud AutoML, and BigQuery ML for multi-cloud reasoning.
- Ability to mentor peers, collaborate across Sales, Customer Success, and Product, and communicate with technical and business stakeholders.
- BA/BS in Computer Science, Mathematics, or a related technical field is preferred, or equivalent practical experience.
- Preferred experience includes RLHF, advanced fine-tuning, hybrid AI architectures, Hugging Face, ML engineering, data science, AI architecture, JIRA, and Agile delivery practices.
Benefits
- Remote-first work arrangement with full-time employment in Mexico or Colombia and contractor arrangements available in other LATAM countries.
- Unlimited vacation and flexible working options.
- Health insurance and parental leave.
- Employee stock option plan.
- Home office allowance and professional development stipend.
- Peer recognition program.
