Related skills
docker aws kubernetes tensorflow pytorch📋 Description
- Lead end-to-end ML assessments across infra, data pipelines, and lifecycle.
- Partner with sales and solutions to shape technical statements of work.
- Serve as senior technical authority; provide architectural guidance.
- Own or orchestrate high-quality POCs to build customer confidence.
- Advise on ML operations standards and production monitoring.
- Shape Caylent's strategy for technically complex opportunities.
🎯 Requirements
- 10+ years in ML/AI with client-facing leadership.
- Deep AWS ML and GenAI knowledge; lifecycle decisions.
- Expertise in multiple ML domains: CV, NLP, time-series, etc.
- Architect and govern production ML systems; translate ML ops into standards.
- Foundation models: LoRA/QLoRA/PEFT, RLHF, quantization, multi-agent on AWS.
- Independent in complex client environments; translate ML tradeoffs to business value.
🎁 Benefits
- 100% remote work
- Private Health Insurance
- Flexible Time Off
- Competitive phantom equity
- Paid for exams and certifications
- Annual stipend for Learning and Development
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