Technical Lead - AI OPs

Key Responsibilities And Duties

• Design and implement a robust, enterprise-grade AIOps platform supporting production operations teams across the full incident lifecycle — from initial observation through engagement and resolution.
• Integrate observability platforms such as Dynatrace, Moogsoft, and Splunk with AI/ML capabilities to enable early anomaly detection, trend analysis, and actionable operational insights.
• Build and maintain an agentic AI-powered virtual assistant that delivers instant, intelligent responses to operational queries — including incident summaries, root-cause analysis, and recommended remediation steps.
• Design and maintain interactive dashboards providing real-time visibility into key operational metrics such as MTTA and MTTR, enabling proactive decision-making across engineering and leadership teams.
• Collaborate with the GAIT Center of Excellence to implement GenAI-based solutions that automate report generation and streamline operational workflows. Partner with Lines of Business development teams to automate routine tasks and reduce manual intervention.
• Architect and implement AI/ML algorithms tailored to boost IT operational efficiency, predict system failures, and recommend preventive actions before issues impact end users.
• Lead, mentor, and develop a team of Site Reliability and AIOps engineers. Engage effectively with both technical and non-technical stakeholders to ensure AIOps tools are understood, valued, and widely adopted across the organization.

Team Leadership and Stakeholder Engagement

• Lead, mentor, and develop a team of Site Reliability and AIOps engineers.
• Engage effectively with both technical and non-technical stakeholders to ensure AIOps tools are understood, valued, and widely adopted across the organization.

Physical Requirements

Sedentary Work

Skills

• At least 5 years of experience in a Technology Role
• Experience with programming languages - Python and/or Java
• Experience, hands-on with cloud platforms (AWS, Azure, or Google Cloud)
• Experience using observability tools like Moogsoft, Dynatrace, or Splunk

Preferred Skills

• At least 7+ years of experience in a technology role
• Familiarity with data processing frameworks such as Apache Kafka and automation tools such as Ansible Tower
• Solid understanding of machine learning algorithms and data analysis techniques
• Practical experience designing and working with agentic systems and automation agents
• Demonstrated ability to lead and develop high-performing engineering teams
• Excellent problem-solving, communication, and interpersonal skills
• Master’s degree in computer science, Data Science, AI/ML, or a related field
• Professional certifications in cloud, AI/ML, or data engineering (e.g., AWS Certified Machine Learning Specialty, Google Professional Data Engineer)
• Experience with DevOps practices and CI/CD pipelines
• Familiarity with Agile development methodologies