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3Pillar Global Himalayas_search · Posted yesterday

Architect / Principal, AI ML Engineer

Canada USD Full time Remote

Senior Principal-AI-ML-Engineer, AI-ML-Technical-Architect, Principal-AI-Architect, Senior-AI-ML-Architect-Jobs, Principal-Machine-Learning-Architect, Lead-AI-ML-Engineer, Principal-ML-Engineer, Senior-AI-ML-Engineer, Principal-AI-Engineer, Senior-AI-ML-Data-Engineer Himalayas_search
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Indexed description

3Pillar is seeking an experienced AI/ML Architect to lead the technical direction and blueprint for scalable AI solutions across multiple domains. The ideal candidate will have a strong grasp of AI architecture patterns and hands-on experience with Gen AI APIs, prompt engineering, and embeddings.

Requirements

  • Define end-to-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps.
  • Serve as a strategic advisor / consultant to clients, leading solution design discussions, presenting AI/ML architectures, and representing 3Pillar in client-facing interactions to drive innovation and business value.
  • Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI).
  • Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs (OpenAI, Gemini and others) or open source models like LLama.
  • Design retrieval-augmented generation (RAG) systems with vector databases (Pinecone, Weaviate, FAISS and similar).
  • Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining.
  • Evaluate build-vs-buy decisions and benchmark AI tools/platforms.
  • Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space and recommend adoption strategies.
  • Mentor technical teams and guide solution architects, AI/ML engineers.
  • Ensure ethical and responsible AI practices including bias detection, interpretability, and governance.
  • Act as a subject matter expert, providing guidance and mentorship to junior colleagues and strengthen the expertise in the organization.
  • Collaborate with leadership to align machine learning strategies with overall business objectives.
  • Represent the organization at industry conferences, workshops, and events.
  • Drive cross-functional collaboration to identify opportunities for applying machine learning to business challenges.

Benefits

  • Flexible work environment (remote, office, or hybrid)
  • Generous time off
  • Mental health plans (country-dependent)
  • Fitness offerings
  • Career growth and development opportunities
  • Professional services model
  • Equal opportunity employer
  • Diversity and inclusion commitment

Originally posted on Himalayas

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