Applied AI ML Engineer - Ottawa, ON

Trend Micro•Ottawa, ON

1d•CA$105,000 - CA$130,000•Hybrid

About The Position

TrendAI™, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk. At TrendAI™, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI-powered future. AI Fearlessly. Position Summary TrendAI builds AI-powered cybersecurity solutions protecting millions of customers worldwide. We’re looking for Applied AI/ML Engineers to ship production AI features on the Trend Vision One™ Platform — from model training to GenAI system design and deployment.

Requirements

Minimum of 2+ years of professional experience in Machine Learning Engineering or Generative AI development, with a documented track record of delivering production-grade systems.
Deep technical foundation in machine learning principles, including model evaluation methodologies, hyperparameter optimization, regularization techniques, and statistical analysis.
Demonstrated success in developing LLM-powered applications or autonomous agentic systems.
Advanced proficiency in Python and established deep learning frameworks such as PyTorch or TensorFlow.
Strong software engineering fundamentals, including rigorous unit/integration testing, Git-based version control, and collaborative code review practices.

Nice To Haves

Hands-on experience with orchestration layers such as LangChain, LangGraph, or MCP.
Proven ability to fine-tune open-source models (e.g., Llama, Mistral, BERT, T5) and implement sophisticated prompt engineering strategies.
Experience with large-scale data processing via Apache Spark/PySpark and deployment on major cloud platforms (AWS, GCP, or Azure).
Proficiency in GPU optimization, including CUDA kernel development, TensorRT, and hardware-aware model compression.
Professional background in cybersecurity or familiarity with industry frameworks such as MITRE ATT&CK or NIST CSF.

Responsibilities

Lead the design and implementation of sophisticated AI agent architectures, incorporating tool-calling capabilities, long-horizon reasoning, advanced memory management, and Retrieval-Augmented Generation (RAG).
Develop and deploy complex multi-agent systems utilizing industry-standard patterns—including ReAct, Plan-and-Execute, and reflection loops—to automate intricate security analysis and operations.
Strategically select, integrate, and fine-tune Large Language Models (LLMs) and Small Language Models (SLMs) tailored for domain-specific applications, ensuring an optimal balance between inference latency, operational cost, and predictive accuracy.
Architect and sustain comprehensive machine learning pipelines, encompassing the full lifecycle from data ingestion and feature engineering to model training, rigorous evaluation, and production deployment.
Enhance model performance through advanced optimization techniques such as quantization, pruning, and TensorRT; maintain robust MLOps infrastructure and manage the model lifecycle pipelines
Leverage deep learning, time-series analysis, and Graph Neural Networks (GNNs) to enhance threat detection, classification, and proactive risk assessment within the cybersecurity domain.
Establish automated evaluation frameworks and performance benchmarks aligned with business KPIs to monitor and improve model quality and reliability over time.