Senior Machine Learning Developer I

Were looking for a Senior ML Developer to join our AIOps Platform 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.

• 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/PubSub 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.
  

• Bachelors 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 BigQuery 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 PubSub).
• 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 TensorFlow.
• 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.

• Experience with telecom industry data and network architecture.
• Post-secondary degrees or certificates in Statistics Mathematics and Data Science.
• Experience deploying secure agentic workflows with cost management and decision traceability.

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