Senior Network Automation Engineer

Our client, a Banking company, is looking for a Senior Network Automation Engineer for their Jersey City, NJ/Hybrid location.

Responsibilities:

• The Senior Network Automation Engineer is responsible for building and maintaining automation, AI, and machine learning capabilities that enable intelligent network event handling, incident self-healing, predictive insights, and AI-assisted troubleshooting.
• This role focuses on developing backend services and workflows that analyze network signals, correlate events, and trigger automated diagnostics or remediation actions that Operations teams can trust and adopt.
• The role is hands-on and software-centric, combining network engineering, applied AI/ML, MLOps, and close partnership with Network Operations, Monitoring, Observability, and Data Science teams.
• 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, safe remediation actions, and guided recovery steps for recurrent network issues. Develop backend automation services using Python and Django, with supporting data stores (e.g., Redis) for state management and caching. Integrate automation services with monitoring, telemetry, and alerting systems to consume events and metrics in near real time. Partner with Network Operations teams to identify repeatable operational patterns suitable for automation. Work closely with Monitoring and Telemetry engineers to ensure automation logic is driven by reliable data signals. Ensure automation workflows include guardrails, validation, and rollback logic to support safe use in production environments. Contribute to automation lifecycle practices including testing, versioning, deployment, and operational support. Provide support and troubleshooting for automation services used in production. Document automation designs, runbooks, and usage guidelines for Operations teams. Design and productionize machine learning solutions for anomaly detection, event correlation, incident classification, root-cause analysis, capacity forecasting, and predictive maintenance. Engineer and curate network telemetry datasets; perform feature engineering, labeling, data-quality validation, and model-ready transformation across metrics, logs, events, traces, and topology data. Develop AI-assisted troubleshooting capabilities using large language models, retrieval-augmented generation, embeddings, and enterprise knowledge sources while maintaining human oversight and deterministic controls for production actions. Build and maintain MLOps pipelines for experiment tracking, model validation, versioning, deployment, monitoring, retraining, and rollback. Evaluate models using measurable operational outcomes, including precision, recall, false-positive reduction, resolution accuracy, latency, reliability, and operational adoption. Implement responsible AI controls covering explainability, privacy, security, bias assessment, auditability, access control, model drift, and safe fallback behavior. Create APIs and reusable services that integrate AI/ML inference with event-management, observability, workflow-orchestration, and IT service-management platforms.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Experience Range: 8-12 years of experience in network engineering, network operations, automation, telemetry, monitoring, data engineering, or applied analytics, including demonstrated experience delivering AI/ML-enabled solutions into production.
• Foundational Skills
• Strong software development experience in Python. 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.
• Experience integrating automation with monitoring, alerting, or telemetry systems.
• Ability to design automation that supports operational reliability and safety, not just orchestration.
• Experience working in production environments with high availability and failure sensitivity.
• Strong communication skills to collaborate with both engineering and operations teams.
• AI/ML Skills and Experience
• Strong understanding of supervised and unsupervised machine learning, time-series analysis, anomaly detection, classification, clustering, forecasting, model evaluation, and feature engineering.
• Hands-on experience with Python data and ML libraries such as pandas, NumPy, scikit-learn, and at least one deep-learning framework such as PyTorch or TensorFlow.
• Experience developing and consuming generative AI solutions, including prompt engineering, embeddings, vector search, retrieval-augmented generation, tool/function calling, and agentic workflow patterns.
• Experience with AI/ML development and operational tooling such as Jupyter, MLflow or equivalent experiment/model management, model-serving APIs, containers, Kubernetes, and CI/CD pipelines.
• Familiarity with vector databases or enterprise search platforms and with frameworks such as LangChain, Semantic Kernel, or equivalent orchestration libraries.
• Ability to validate model quality, explain model behavior, monitor performance and drift, tune thresholds, and translate model outputs into safe operational decisions.
• Experience handling high-volume streaming or near-real-time telemetry using message queues, event streaming, and scalable data-processing patterns.
• Knowledge of responsible AI, secure AI engineering, data privacy, model governance, adversarial risk, and human-in-the-loop controls in regulated production environments.
• 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, observability, incident reduction, or autonomous operations.
• Experience with cloud AI/ML services, GPU-enabled workloads, or enterprise model platforms.
• Familiarity with graph analytics or knowledge graphs for topology-aware correlation and root-cause analysis.
• Experience designing evaluation datasets, offline and online tests, and operational acceptance criteria for AI-assisted automation.
• Must Have
• Technical
• Python
• REST API
• ServiceNow

Why Should You Apply?

• Health Benefits
• Referral Program
• Excellent growth and advancement opportunities