Machine Learning Engineer Data Science / AI Toronto, Canada

September 5, 2025

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Job Description

Description

Haptiq is a leader in AI-powered enterprise operations, delivering digital solutions and consulting services that drive value and transform businesses. We specialize in using advanced technology to streamline operations, improve efficiency, and unlock new revenue opportunities, particularly within the private capital markets.

Our integrated ecosystem includesPaaS – Platform as a Service, the Core Platform, an AI-native enterprise operations foundation built to optimize workflows, surface insights, and accelerate value creation across portfolios;SaaS – Software as a Service , a cloud platform delivering unmatched performance, intelligence, and execution at scale;S&C – Solutions and Consulting Suite , modular technology playbooks designed to manage, grow, and optimize company performance. With over a decade of experience supporting high-growth companies and private equity-backed platforms, Haptiq brings deep domain expertise and a proven ability to turn technology into a strategic advantage.

About the Role

We’re looking for aninnovative ML Engineer to join our AI team and help design, prototype, and operationalize advanced AI systems, includingRetrieval-Augmented Generation (RAG) pipelines, MCP services, and agentic workflows . You’ll work closely with Python Developers and MLOps Engineers to bring AI-powered features from concept to production.

Key Responsibilities

  • Design, prototype, and iterate on AI models, RAG pipelines, MCP services, and agentic reasoning systems.
  • Develop AI-powered services using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, Haystack, and DSPy.
  • Build and optimize prompts for AI-driven tasks like chat, summarization, information extraction, and decision support.
  • Integrate and tune vector databases (e.g., Chroma, FAISS, Pinecone, Qdrant) to optimize vector search workflows.
  • Collaborate with Python Developers to define service interfaces, APIs, and operational requirements.
  • Implement task decomposition, memory management, and tool-use strategies within agentic systems.
  • Monitor AI system performance, operational risks, and prompt evaluation metrics.
  • Stay current with emerging AI frameworks, architectures, and best practices in AI observability and evaluation.

Required Skills & Experience

  • Strong Python development skills with an emphasis on clean, scalable, and modular code.
  • Hands-on experience working withLLMs and agent frameworks .
  • Solid understanding ofRAG architecture, vector search concepts (cosine similarity, MMR, late interaction), and system design.
  • Experience buildingmulti-step agentic workflows and reasoning systems.
  • Familiarity with AI performance metrics, model drift detection, and operational monitoring.
  • Awareness of AI limitations, including latency, hallucination risks, and explainability challenges.
  • Experience withDSPy or other prompt optimization tools is a plus.
  • A curious, experimental mindset with a passion for testing emerging AI tools and frameworks
  • Flexible work arrangements (including hybrid mode)
  • Great Paid Time Off (PTO) policy
  • Opportunities for professional growth and development.
  • A supportive, dynamic, and inclusive work environment.

Why Join Us?

We value creative problem solvers who learn fast, work well in an open and diverse environment, and enjoy pushing the bar for success ever higher. We do work hard, but we also choose to have fun while doing it.

Job ID | Posted on June 27, 2025

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Haptiq does not discriminate on the basis of race, sex, color, religion, age, national origin, marital status, disability, veteran status, genetic information, sexual orientation, gender identity or any other reason prohibited by law in provision of employment opportunities and benefits.

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Company

Heart Talent

Location

Toronto

Country

Canada

Salary

150.000

URL

https://en-ca.whatjobs.com/coopob__cpl___291_2629399__3337?utm_source=3337&utm_medium=feed&keyword=Machine-Learning-Engineer&location=Toronto&geoID=6225