Applied AI ML Engineer - Ottawa, ON

Trend Micro•Ottawa, ON

23d•$105,000 - $130,000•Hybrid

About The Position

Trend Micro, a global cybersecurity leader, helps make the world safe for exchanging digital information across enterprises, governments, and consumers. Fueled by decades of security expertise, global threat research, and continuous innovation, Trend harnesses AI to protect organizations and individuals across clouds, networks, devices, and endpoints. The Trend Vision One™ enterprise cybersecurity platform accelerates proactive security outcomes by predicting and preventing threats across the entire digital estate and environments like AWS, Google, Microsoft, and NVIDIA. Proactive security starts here. TrendMicro.com Location: This is a hybrid role with a minimum in-office requirement of 3 days per week in the Ottawa, ON office located at 40 Hines Rd, Ottawa, ON K2K 2M5. 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

Architecting Autonomous Systems: 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).
Orchestrating Multi-Agent Workflows: 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.
Model Selection & Optimization: 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.
End-to-End Pipeline Management: 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.
Inference & Infrastructure Engineering: Enhance model performance through advanced optimization techniques such as quantization, pruning, and TensorRT; maintain robust MLOps infrastructure and manage the model lifecycle pipelines
Advanced Cybersecurity Analytics: Leverage deep learning, time-series analysis, and Graph Neural Networks (GNNs) to enhance threat detection, classification, and proactive risk assessment within the cybersecurity domain.
Performance Benchmarking: Establish automated evaluation frameworks and performance benchmarks aligned with business KPIs to monitor and improve model quality and reliability over time.