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AI/ML Scientist
Maya HTTAI/ML Scientist building RAG-based LLM architectures for Maya HTT, Canada’s industrial AI solutions provider. Deploying production ML systems for predictive maintenance, smart factories, and engineering decision-making.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and building RAG-based LLM architectures, integrating AI/ML systems for engineering and manufacturing challenges, and leading the full ML cycle from ideation to deployment. Proficient in collaborating with cross-functional teams to drive actionable insights and performance tracking.
Highest-signal resume keywords
RAG PipelinesPython ProgrammingDeep LearningMLOpsPredictive Maintenance
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
RAG PipelinesPython ProgrammingDeep LearningTime-Series ModelingAnomaly DetectionMachine LearningModel MonitoringDeploymentKnowledge ExtractionNLP Methods
Soft Skills
CollaborationCommunication
Tools & Technologies
PyTorchHugging FaceLangChainVector DatabasesQDrantFAISSPinecone
Industry Keywords
AI SystemsIndustrial DataTechnical DocumentationSmart FactoriesDigital TwinsManufacturing Environment Constraints
Tech Stack
Tools & technologiesPythonPyTorch
About the role
Key responsibilities & impact- Design and build RAG-based LLM architectures combining industrial data and technical documentation with generative AI
- Collaborate with industrial engineers, data scientists, and domain experts to deploy AI systems in production
- Lead the full ML cycle from ideation through MLOps and long-term performance tracking
- Communicate insights and models that drive actionable outcomes
- Develop AI/ML systems for engineering and manufacturing challenges, including predictive maintenance, smart factories, and AI-driven decision-making
Requirements
What you’ll need- Hands-on expertise in RAG pipelines, including LLMs, vector databases, and retrievers
- Strong Python and deep learning skills
- Experience with PyTorch, Hugging Face, and LangChain
- Fluency in time-series modeling and anomaly detection
- Experience applying machine learning to physical systems
- Real-world experience with ML in production, including MLOps, model monitoring, and deployment
- Ability to bridge advanced AI with complex engineering and manufacturing domains
- Experience with agentic frameworks
- Nice to have: deep understanding of vector databases such as QDrant, FAISS, and/or Pinecone
- Nice to have: experience with knowledge extraction and NLP methods
- Nice to have: familiarity with digital twins, industrial systems, and manufacturing environment constraints
- Authorization to work in the country where the job is located
Benefits
Comp & perks- Flex Working Hours and Hybrid Work
- Permanent Position
- 100% Employer-Paid Benefits starting from Day One: Medical, dental, life, short/long term disability insurances
- Career Growth Opportunities and flexible career paths
- Learning Opportunities and professional development
- Generous Time-Off Policy with excellent and flexible PTO Policy
- Structured Onboarding Program
- Team support and assistance from Day One
- Equal opportunity employer
- Accommodations available upon request during the hiring and selection process