Nokia
As a Principal Engineer, you will serve as both a technical authority and strategic thought leader, responsible for architecting end-to-end MLOps frameworks that enable real-time, closed-loop automation, predictive maintenance, anomaly detection, network optimization, and intelligent orchestration of 4G/5G core components. You will bridge the gap between data science, software engineering, DevOps, and telecom domain expertise, creating the robust systems needed to transition from traditional reactive operations to AI-native, self-optimizing networks (SON).
You will collaborate closely with network architects, data scientists, DevOps teams, and cloud/edge infrastructure engineers to ensure ML models are not only accurate but also scalable, explainable, observable, and production-ready — capable of running on-premises, in the cloud, and at the mobile edge.
Qualifications
Bachelor’s or master’s degree in computer science, Electrical Engineering, Telecommunications, or related field.
12+ years of experience in software engineering or DevOps, with 3+ years focused on MLOps.
Strong expertise in machine learning platforms (e.g., MLflow, TFX, SageMaker, Vertex AI).
Deep understanding of 5G architecture and protocols, including RAN, core, and MEC.
Proficiency with containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform, Helm).
Experience with cloud platforms such as AWS, Azure, or GCP.
Solid programming skills in Python, with familiarity in Bash, Go, or Java.
Familiarity with network telemetry, edge AI, and real-time ML workloads.
It would be good if you had:
Knowledge of AI/ML use cases in telecom such as traffic forecasting, anomaly detection, beamforming optimization, etc.
Contributions to open-source Machine Learning Operations tools or telecom frameworks.
Responsibilities
Architect and implement resilient MLOps pipelines for the entire AI lifecycle, from data ingestion to model deployment and continuous monitoring in production, leveraging Kubernetes, Kubeflow, and cloud-native services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
Design and manage end-to-end automated deployment strategies for AI-powered network functions, embracing GitOps principles and advanced IaC tools like Pulumi or Crossplane for deterministic and auditable infrastructure.
Establish and refine comprehensive observability frameworks for AI models and integrated network systems, utilizing distributed tracing (e.g., Open Telemetry), real-time anomaly detection, and predictive analytics to ensure proactive performance management and explainability.
Drive the adoption of advanced CI/CD techniques specifically tailored for AI model versioning, testing, and A/B experimentation within a highly regulated telecom environment, ensuring rapid iteration and reliable delivery.
Drive the adoption of AI-native network principles, leveraging intent-based networking and self-optimizing network (SON) capabilities for autonomous network operations and enhanced subscriber experience.
Possess expert-level understanding of 4G & 5G core network protocols (e.g., HTTP/2, SCTP, QUIC, gRPC for 5GC, Diameter for 4G) and their impact on data plane and control plane optimization through AI.
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You will collaborate closely with network architects, data scientists, DevOps teams, and cloud/edge infrastructure engineers to ensure ML models are not only accurate but also scalable, explainable, observable, and production-ready — capable of running on-premises, in the cloud, and at the mobile edge.
Qualifications
Bachelor’s or master’s degree in computer science, Electrical Engineering, Telecommunications, or related field.
12+ years of experience in software engineering or DevOps, with 3+ years focused on MLOps.
Strong expertise in machine learning platforms (e.g., MLflow, TFX, SageMaker, Vertex AI).
Deep understanding of 5G architecture and protocols, including RAN, core, and MEC.
Proficiency with containerization (Docker), orchestration (Kubernetes), and infrastructure as code (Terraform, Helm).
Experience with cloud platforms such as AWS, Azure, or GCP.
Solid programming skills in Python, with familiarity in Bash, Go, or Java.
Familiarity with network telemetry, edge AI, and real-time ML workloads.
It would be good if you had:
Knowledge of AI/ML use cases in telecom such as traffic forecasting, anomaly detection, beamforming optimization, etc.
Contributions to open-source Machine Learning Operations tools or telecom frameworks.
Responsibilities
Architect and implement resilient MLOps pipelines for the entire AI lifecycle, from data ingestion to model deployment and continuous monitoring in production, leveraging Kubernetes, Kubeflow, and cloud-native services (e.g., AWS SageMaker, Azure ML, GCP Vertex AI).
Design and manage end-to-end automated deployment strategies for AI-powered network functions, embracing GitOps principles and advanced IaC tools like Pulumi or Crossplane for deterministic and auditable infrastructure.
Establish and refine comprehensive observability frameworks for AI models and integrated network systems, utilizing distributed tracing (e.g., Open Telemetry), real-time anomaly detection, and predictive analytics to ensure proactive performance management and explainability.
Drive the adoption of advanced CI/CD techniques specifically tailored for AI model versioning, testing, and A/B experimentation within a highly regulated telecom environment, ensuring rapid iteration and reliable delivery.
Drive the adoption of AI-native network principles, leveraging intent-based networking and self-optimizing network (SON) capabilities for autonomous network operations and enhanced subscriber experience.
Possess expert-level understanding of 4G & 5G core network protocols (e.g., HTTP/2, SCTP, QUIC, gRPC for 5GC, Diameter for 4G) and their impact on data plane and control plane optimization through AI.
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