ML Engineer

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We are Optimum, a leader in the fast-paced world of connectivity, and we're seeking driven and enthusiastic professionals to join our team, empower lives, fuel businesses, and drive innovation. Connectivity is now longer a luxury, but a necessity. A career at Optimum means you'll be enabling progress and enhancing lives by providing reliable, high-speed connectivity solutions that keep the world connected. Our successes, now and in the future, are powered by our amazing product, a commitment to our people and culture, and the connections we make in our communities.

If you are resourceful, collaborative, and passionate about delivering consistent excellence, Optimum is for you! 

Job Summary

As a Senior SOC Engineer (AI & Automation), you will design, build, and operate the AI, automation, and detection-engineering capabilities that power our Security Operations Center. Bridging security operations and software engineering, you will develop AI-driven detection, triage, and response tooling, integrate large language model (LLM) and agentic workflows into analyst operations, and ensure those capabilities are accurate, safe, measurable, and continuously improved. As a senior member of the team, you will also serve as a technical leader during major security incidents, leading investigations and turning lessons learned into stronger detections, automations, and playbooks.

Responsibilities

•    Design, build, and maintain AI/ML- and automation-driven capabilities for alert enrichment, correlation, summarization, triage, and prioritization.
•    Develop and maintain SOAR automations and detection-as-code pipelines that are version-controlled, tested, and peer-reviewed.
•    Integrate LLM and agentic AI tooling into SOC workflows (copilots, auto-triage agents); engineer prompts, guardrails, and evaluation harnesses.
•    Evaluate, benchmark, and tune AI models and tools for security use cases, measuring precision and recall, false-positive reduction, and impact on mean time to detect and respond (MTTD/MTTR).
•    Build data pipelines and feature engineering from security telemetry to support detection and machine-learning use cases.
•    Apply MLOps practices, including model versioning, monitoring, drift detection, and retraining, to security models running in production.
•    Ensure responsible and secure AI use, including data governance, prompt-injection and model-abuse defenses, privacy, and output validation.
•    Partner with detection engineers, SOC analysts, and incident responders to operationalize tooling and feed lessons learned back into models and automations. •    Serve as a senior escalation point and incident commander for complex and major incidents, coordinating cross-functional response and directing technical workstreams. •    Lead investigations and forensic analysis for escalated incidents and provide hands-on incident response support across on-premises and cloud environments. •    Own post-incident reviews and root cause analyses, translating lessons learned into new detections, automations, and playbook improvements. •    Develop, run, and mature incident-response playbooks and tabletop exercises (TTX) to validate and improve organizational readiness. •    Define and report detection and incident-response metrics (e.g., MTTD, MTTR) to measure and continuously improve SOC effectiveness. •    Mentor analysts and engineers, fostering a culture of continuous learning across AI-augmented and incident-response workflows, and promote an AI-first operating model across the SOC.