Principle Engineer

You will manage a talented team of data scientists and AI engineers, driving the adoption of intelligent automation, predictive analytics, and proactive problem resolution across our complex IT landscape. This position requires a leader with a deep understanding of both data science principles and the intricacies of enterprise IT operations.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or a related quantitative field.
• 10+ years of progressive experience in data science, machine learning, and/or AI engineering.
• 5+ years of experience in a leadership or management role, leading technical teams focused on data science or AI.
• Proven experience in designing, developing, and deploying AI/ML models for real-world applications, particularly within IT operations or related domains (e.g., observability, security, infrastructure management).
• Strong understanding of IT operations concepts, including monitoring, alerting, incident management, change management, and IT service management (ITSM).
• Proficiency in programming languages commonly used in data science and AI (e.g., Python, Scala, Java).
• Hands-on experience with big data technologies (e.g., Spark, Hadoop, Kafka) and cloud platforms (AWS, Azure, GCP).
• Solid grasp of machine learning algorithms (e.g., supervised, unsupervised, deep learning) and statistical modeling.
• Excellent communication, interpersonal, and leadership skills with the ability to articulate complex technical concepts to non-technical stakeholders.
• Demonstrated ability to drive strategic initiatives, manage complex projects, and deliver results in a fast-paced environment.

Preferred Qualifications:

• Experience with specific AIOps platforms or tools (e.g., Splunk, Dynatrace, Moogsoft, PagerDuty, ServiceNow, Datadog, ELK stack).
• Familiarity with IT service management frameworks (e.g., ITIL).
• Experience with containerization (Docker, Kubernetes) and microservices architectures.
• Knowledge of MLOps best practices and tools for automating and managing the ML lifecycle.

• Experience in a large-scale enterprise environment with diverse and complex IT infrastructure.

• Strategic Leadership: Define and execute the AIOps strategy and roadmap, aligning it with overall IT and business objectives. Identify opportunities to leverage AI/ML for enhanced IT observability, incident management, performance optimization, and automation.

• Team Management & Development: Lead, mentor, and grow a high-performing team of data scientists and AI engineers. Foster a culture of innovation, continuous learning, and technical excellence.
• Solution Design & Development: Oversee the end-to-end design, development, and deployment of AIOps solutions, including anomaly detection, predictive failure analysis, root cause analysis, intelligent alerting, and automated remediation.
• Cross-Functional Collaboration: Partner closely with IT Operations, Site Reliability Engineering (SRE), Network Engineering, Application Development, and other stakeholders to understand operational challenges and deliver impactful AI-driven solutions.
• Data & Platform Management: Ensure the availability, quality, and governance of operational data necessary for AI/ML model training and inference. Drive the selection, integration, and optimization of AIOps platforms and tools.
• Model Lifecycle Management: Establish robust MLOps practices for model development, testing, deployment, monitoring, and retraining to ensure the continuous effectiveness and reliability of AI models in production.
• Innovation & Research: Stay abreast of the latest advancements in AI/ML, AIOps, and IT operations. Drive research and experimentation to explore new techniques and technologies that can further enhance our operational intelligence.
• Performance & Metrics: Define key performance indicators (KPIs) for AIOps initiatives and regularly report on the impact and value delivered to the organization.
• Budget & Resource Management: Manage project budgets, resources, and timelines effectively to ensure successful delivery of AIOps programs.