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Principal AI/ML Architect

3 weeks, 1 day ago
Mexico
Full-time
Lead
AI (Artificial Intelligence)
Software Development
Apache Airflow Apache Spark AWS AWS CDK CI/CD CloudFormation Computer Vision Dagster Docker Feature Engineering Generative AI Kafka Kubernetes LLM Machine Learning MLflow MLOps NLP PyTorch Reinforcement Learning SageMaker Scikit-learn TensorFlow Terraform Transformers XGBoost
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Caylent

Caylent

Caylent is a cloud native services company that simplifies AWS cloud adoption with a user-friendly DevOps platform for scalable cloud environments.

IT Services
251-1K
Founded 2015
$16M raised
View All Jobs 13

Description

  • Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness.
  • Partner with sales and solutions teams during proposal and scoping to shape statements of work.
  • Serve as the senior technical authority across client engagements, providing architectural guidance and technical quality oversight.
  • Own or orchestrate high-quality proofs of concept that help customers validate larger ML initiatives.
  • Advise customers on MLOps, LLMOps, model lifecycle management, and production monitoring architecture.
  • Translate operational and architectural complexity into guardrails and decisions that customer teams can sustain.
  • Contribute technical credibility and architectural thinking to help win complex ML opportunities.
  • Support the ML practice through peer guidance, technical interviews, accelerators, reference architectures, and thought leadership.

Requirements

  • 10+ years of experience in machine learning or AI, with client-facing consulting or advisory leadership experience.
  • Deep, current knowledge of the AWS ML and GenAI ecosystem.
  • Ability to make and defend architectural decisions across the full ML lifecycle, from data and feature engineering through training, deployment, and monitoring.
  • Deep expertise in at least two or three ML domains such as traditional ML, computer vision, NLP, or time series.
  • Proven ability to architect and govern production ML systems end-to-end, including MLOps and LLMOps.
  • Deep expertise in foundation model adaptation, including LoRA, QLoRA, PEFT, RLHF, DPO, quantization, vLLM, DeepSpeed, and FSDP.
  • Experience with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS.
  • Ability to operate independently in complex customer environments and communicate ML tradeoffs to both technical and executive audiences.
  • AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional preferred.
  • Experience shaping practice-level standards, reference architectures, and reusable ML accelerators preferred.
  • Exposure to varied industries and problem types in a consulting or client-facing context preferred.
  • Deep fluency in responsible AI practices, including model evaluation, bias detection, fairness frameworks, and AI governance preferred.
  • Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production preferred.
  • Visa sponsorship is not available now or in the future.

Benefits

  • 100% remote work.
  • Private health insurance.
  • Flexible time off.
  • Competitive phantom equity.
  • Paid exams and certifications.
  • Peer bonus awards.
  • State-of-the-art laptop and tools.
  • Equipment and office stipend.
  • Individual professional development plan.
  • Annual learning and development stipend.

Interested in this position?

Apply directly on the company website

Apply Now

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