AI / ML Engineer

Zinkworks; Edmonton; On-site; CAD 80,000 - 120,000; Full time

Job summary

An innovative firm is seeking an experienced AI/ML Engineer to join their Solutions & Innovation team. This role focuses on designing and implementing AI models for capital markets and telecom networks, tackling diverse challenges such as anomaly detection and network optimization. You will play a key role in helping clients leverage advanced AI capabilities for real-time insights. The ideal candidate will have strong Python skills and experience with Google Cloud Platform, along with a passion for collaboration and delivering impactful solutions. This position offers an exciting opportunity to work at the forefront of technology in a dynamic environment.

Qualifications

• Strong proficiency in Python with ML frameworks like TensorFlow or PyTorch.
• Hands-on experience with Google Cloud Platform services, especially Vertex AI.

Responsibilities

• Design, build, and refine AI/ML models for telecom and financial systems.
• Collaborate with cross-functional teams to deliver proof-of-concept solutions.

Skills

Python

Machine Learning (ML)

Time-series modeling

Anomaly detection

Data analysis

Tools

Google Cloud Platform (GCP)

TensorFlow

PyTorch

REST APIs

Docker

Job description

About Zinkworks

At Zinkworks, we’re transforming industries by creating cutting-edge solutions that fuse AI, ML, and intelligent automation into real-world applications. We're passionate about innovation, quality, and building high-performance teams that drive impactful change across telecommunications, finance, and emerging tech.

About the Role

We are looking for an experienced and motivated AI/ML Engineer to join our Solutions & Innovation team. This role involves designing and implementing AI models for proof-of-concepts and project solutions within two key verticals: capital markets and telecom networks.

You’ll work on a diverse range of use cases, including anomaly detection in RAN (Radio Access Network) time-series data, network optimization, and time-series modeling in financial systems. Your work will be instrumental in helping our clients—ranging from telecom operators to global banks—leverage advanced AI/ML capabilities for real-time insights and predictive intelligence.

Key Responsibilities

• Model Development & Experimentation
  • Design, build, and refine AI/ML models using architectures such as LSTM, Transformer, and GCN (Graph Convolutional Networks).
  • Apply anomaly detection techniques (e.g., Isolation Forest, statistical methods) to multivariate time-series data.
• Data Engineering & Processing
  • Work with structured and semi-structured data from telecom and financial systems.
  • Develop data ingestion and transformation pipelines using GCP services (e.g., Cloud Functions, BigQuery).
• Deployment & Integration
  • Deploy scalable models to production using GCP tools such as Vertex AI and Cloud Run.
  • Develop REST APIs to expose inference services for integration with client systems.
• Collaboration & Delivery
  • Collaborate with cross-functional teams including solution architects, cloud engineers, and domain experts.
  • Participate in client workshops and technical governance for PoC delivery and feedback incorporation.
• Monitoring & Validation
  • Define and track model evaluation metrics (e.g., F1-score, ROC-AUC, RMSE).
  • Implement logging, monitoring, and retraining strategies as needed.

Required Skills and Experience

• Strong proficiency in Python, with experience using ML frameworks such as TensorFlow or PyTorch.
• Solid understanding of time-series modeling, anomaly detection, and multivariate data analysis.
• Hands-on experience with Google Cloud Platform (GCP)—especially Vertex AI, BigQuery, Cloud Functions, and Cloud Run.
• Familiarity with RESTful API development and serving ML models in production.

Preferred Qualifications

• Experience working with one or more of the following:
  • Telecom KPIs (e.g., latency, throughput, jitter, SINR)
  • Capital markets datasets (e.g., trade logs, FIX message flows)
• Experience with Graph Neural Networks and Transformer-based models.
• Exposure to OSS/BSS systems, RAN telemetry, or network optimization domains.
• Familiarity with containerization and CI/CD workflows (e.g., Docker, GitHub Actions).
• Prior experience in financial services, especially around trade surveillance or back-office analytics.