Caylent

Caylent

Principal AI/ML Architect

CANADA · Principal

No sponsorship$180k-$223kDetected 60 days ago
ElasticsearchVector DatabasesAWSAzureDockerKubernetesTerraformCI/CDDevOpsSite Reliability EngineeringKafkaMachine LearningTensorFlowPyTorchscikit-learnSparkAirflowData EngineeringNLPComputer VisionLLMsRAGAgentic AILLMOps

About the role

  • This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills.
  • You will shape strategy, influence architecture, and leave every team you touch better than you found it.
  • If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against - and you are looking to do that across a growing portfolio of varied and interesting customers - this is the role for you.

Responsibilities

  • Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness - producing recommendations that drive executive decision-making and earn Caylent the next engagement.
  • Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
  • Deep expertise across foundation model adaptation - fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) - combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS.
  • These tools are designed to support - not replace - human decision-making.

Requirements

  • Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
  • The non-negotiables
  • 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
  • Deep, current knowledge of the AWS ML and GenAI ecosystem, with the 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 - whether traditional ML, computer vision, NLP, time series, or others - combined with the judgment to assess, architect, and advise across the broader ML landscape.
  • Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards and decisions that engineering teams can execute and executives can act on.
  • Deep expertise across foundation model adaptation - fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization (quantization, vLLM), and distributed training (DeepSpeed, FSDP) - combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
  • Proven ability to operate independently in complex customer environments - navigating ambiguity, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.
  • Strong differentiators
  • AWS Certified Machine Learning - Specialty and/or AWS Certified Solutions Architect - Professional.
  • Exposure to varied industries and problem types in a consulting or client-facing context.

Skills

  • Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
  • PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama

Compensation

  • The expected base salary range for this position is CAD $180,000 - $223,000 per year, commensurate with experience and qualifications.
  • In addition to the base salary, the compensation package may include bonuses, commissions, equity, and other incentives.

Benefits

  • 100% Premium Coverage for the employee and dependents
  • Competitive phantom equity
  • Long-Term Disability
  • 4% Pension match (employer contribution)
  • Parental Leave
  • Peer bonus awards
  • Equipment & Office Stipend
  • Annual stipend for Learning and Development

Company info

  • Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
  • Advise customers on ML operations standards and architecture - covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks - translating operational complexity into decisions and guardrails their teams can own and sustain.
  • Shape how Caylent wins its most technically complex opportunities - contributing the architectural thinking and credibility that turns prospects into customers.

Visa & Work Authorization

  • NOTE: We're unable to provide visa sponsorship now or at any time in the future.
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This listing is sourced directly from Caylent's careers page and normalized into a canonical job model.