Senior Machine Learning Developer

Position: Senior Machine Learning Developer I
At Bell, we do more than build world‑class networks, develop innovative services and create original multiplatform media content – we advance how Canadians connect with each other and the world.

If you’re ready to bring game‑changing ideas to life and join a community that values professional growth and employee wellness, we want you on the Bell team.

The Bell Mobility team offers the best and latest mobile devices, wireless services and Internet of Things solutions to consumer and business customers, with the top speeds, coverage and reliability on Canada’s Best National Network. We love to innovate, embrace big challenges, and live for the newest technology

Summary
We’re looking for a Senior ML Developer to join our AIOps Platform team. In this role, you will work closely with Data Scientists to take AI/ML solutions to production. You will accelerate the delivery of our core AI services from proof‑of‑concept to highly available, production‑grade. You will bridge the gap between data science and platform operations, applying robust software engineering practices, intent‑driven development, and MLOps best practices to ensure our systems are scalable, reliable, and performant.

Key Responsibilities
Production ML Engineering:
Collaborate with data science, platform developers and architects, and other teams to operationalize ML models, LLM / Agentic workflows, and advanced analytics.
MLOps & Lifecycle Management:
Own the end-to-end ML lifecycle (feature engineering, training, evaluation, deployment). Enforce MLOps best practices including CI/CD, model versioning, reproducibility, and rollout controls.
Pipeline & Architecture Design:
Build real‑time streaming pipelines using Apache Beam/Dataflow and design robust event‑driven architectures (Kafka/Pub Sub patterns).
Design and build high‑throughput, low‑latency system architectures with strict idempotency and deduplication.
API & Service Delivery:
Lead API and schema design for complex ML‑enabled services.
Quality & Reliability:
Instill a strong testing and quality mindset across the team (test‑driven development, unit/integration tests, code reviews, security checks). Debug and optimize distributed systems for maximum performance.
Infrastructure Management:
Implement Infrastructure as Code (Terraform/CDKTF) to manage scalable, cloud‑native deployments.

Critical Qualifications
Bachelor's degree or higher in Computer Science or Computer Engineering or a related field. Relevant experience may be considered in lieu of a degree.
5+ years of professional software engineering experience, specifically focused on production‑grade AI/ML systems.
Expert-level proficiency in Python software engineering.
Strong experience with the GCP stack (Vertex AI, Big Query, Spanner, GKE, GCS, Pub/Sub).
Deep practical knowledge of MLOps best practices and taking models from proof‑of‑concept to highly available production services.
Hands‑on experience with real‑time data processing (Apache Beam, Dataflow) and event‑driven architecture (Kafka, Pub Sub).
Proven ability in Infrastructure as Code (Terraform/CDKTF) and cloud‑native orchestration (Kubernetes).
Strong software-testing and quality discipline (unit/integration/end‑to‑end/functional testing, code review, security checks)
Hands‑on experience with ML frameworks such as PyTorch, scikit‑learn, XGBoost, or Tensor Flow.

Experience with graph databases / Spanner Graph and Graph Neural Networks (GNN)
Strong communication, analytical, debugging, and cross‑functional collaboration skills.
Ability to write clean, efficient, and reusable code.
Plus if you have experience with distributed / federated query engines such as Trino.

Preferred Qualifications

Experience with telecom industry data and network architecture.
Post‑secondary degrees or…