Network Automation Engineer

Key Responsibilities: Design and develop network automation services for event correlation, intelligent troubleshooting, and automated remediation. Build automation that supports self-healing use cases, such as automated checks and safe remediation actions for recurrent network issues. Develop backend automation services using Python and Django, with supporting data stores like Redis for state management. Integrate automation services with monitoring, telemetry, and alerting systems to consume events and metrics. Partner with Network Operations teams to identify repeatable operational patterns suitable for automation. Ensure automation workflows include guardrails, validation, and rollback logic for safe use in production environments. Design and productionize machine learning solutions for anomaly detection, event correlation, and predictive maintenance. Develop AI-assisted troubleshooting capabilities using large language models and retrieval-augmented generation. Build and maintain MLOps pipelines for model validation, deployment, monitoring, and retraining. Create APIs and reusable services that integrate AI/ML inference with IT service-management platforms. Required

Education: A Bachelor's or Master's degree in Computer Science, Engineering, or a related field is required.

Experience: This position requires 8–12 years of experience in network engineering, network operations, automation, telemetry, monitoring, data engineering, or applied analytics. This must include demonstrated experience delivering AI/ML-enabled solutions into production.

Technical Skills: Strong software development experience in Python, including experience with REST APIs, message queues, or workflow engines. Experience building backend services using Django or similar frameworks. Hands-on experience using Redis or equivalent in-memory data stores. Solid understanding of enterprise network operations, including routing, switching, wireless, and SD-WAN concepts. Strong understanding of machine learning concepts such as time-series analysis, anomaly detection, and classification. Hands-on experience with Python data and ML libraries such as pandas, NumPy, and scikit-learn, plus a deep-learning framework like PyTorch or TensorFlow. Experience developing generative AI solutions, including prompt engineering, embeddings, vector search, and retrieval-augmented generation. Experience with AI/ML development and operational tooling such as Jupyter, MLflow, containers, and Kubernetes.

Additional Information: This role has a hybrid schedule requirement of a minimum of three days onsite per week. This position is a potential contract-to-hire opportunity and is not open to candidates who require sponsorship now or in the future. Preferred

Qualifications: Experience with event correlation, rule-based automation, or decision-oriented workflows. Familiarity with observability data (metrics, logs, events) as automation inputs. Exposure to Infrastructure as Code and CI/CD practices. Understanding of IT service management and incident workflows. Experience applying AIOps techniques to network assurance or autonomous operations. Familiarity with graph analytics or knowledge graphs for root-cause analysis.