Technical Delivery Lead - AIOPs/Automation

Job Title: Technical Delivery Lead — AI Platforms & Enterprise Automation

Department: Application Infrastructure & Operations (AIO)

Reports To: IT Director

Position Overview

We are seeking a high-performing, deeply technical, and exceptionally self-reliant Technical Delivery Lead to orchestrate and drive the execution of core artificial intelligence, AIOps, and infrastructure automation streams across our enterprise environment.

In this role, you will serve as the primary operational driver and technical delivery lead across key automation initiatives—including agentic AI platforms, enterprise observability frameworks, vulnerability tracking, and automated self-healing solutions. Operating with a high degree of autonomy, you will bridge architectural strategy with hands-on technical delivery, coordinating cross-functional engineering teams to ensure milestones are met, financial savings are realized, and executive leadership receives strategic visibility.

Skills:

Key Responsibilities

• Orchestrate Agentic AI & AIOps Delivery: Serve as the technical delivery orchestrator for enterprise-wide Agentic AIOps initiatives. Drive milestone tracking, integration of developed code blocks, and deployment of multi-agent reasoning systems, dynamic agent registries, and tool-calling interfaces utilizing Google Cloud Vertex AI, Model Context Protocol (MCP), and agentic frameworks.
• Lead Observability & Self-Healing Automation: Direct operational delivery for enterprise observability programs. Oversee telemetry ingestion, monitoring standardization, automated alerting, and self-healing automation workflows across multi-cloud infrastructure using enterprise observability tools (e.g., Dynatrace, Splunk, CloudWatch).
• Vulnerability Tracking & Security Remediation: Formulate, integrate, and deploy standardized vulnerability tracking workflows and automated remediation frameworks across application and infrastructure portfolios to maintain strict enterprise compliance and security posture.
• Cross-Functional Team Orchestration: Harmonize diverse engineering, application support, data, and security teams. Manage resource dependencies, navigate competing priorities, and drive strict accountability across separate technical units to meet consolidated timeline goals.
• Monthly Project Reporting & Savings Tracking: Establish quantitative KPIs to track delivery health, token consumption, operational MTTR improvements, and financial value. Build and maintain executive reporting frameworks that explicitly measure and report cost savings generated by AI and automation initiatives.
• Executive Communication & Autonomy: Establish concise, high-level reporting cadences for executive leadership. Anticipate bottlenecks, clear technical roadblocks independently, and distill complex, multi-team technical statuses into clear, actionable executive updates.

Required Qualifications & Technical Skills 

• Education: Master’s degree preferred; Bachelor’s degree in Computer Science, Information Technology, Systems Engineering, or a related field required.
• Experience: 8+ years leading complex technical projects, product delivery, or cloud infrastructure rollouts within a global enterprise IT environment, with at least 2+ years leading AI/AIOps platform deployments.
• Deep AI & Agentic Literacy: Mastery of AI concepts, including Large Language Models (LLMs), Agentic AI, multi-agent reasoning, tool-calling architectures, Retrieval-Augmented Generation (RAG), and the Model Context Protocol (MCP).
• Observability & Cloud Baseline: Hands-on experience with enterprise monitoring platforms (e.g., Dynatrace, Splunk, CloudWatch), cloud environments (GCP, AWS), and CI/CD pipeline automation.
• Systems Thinking & Governance: Proven ability to translate complex I&O environments into contextual schemas for AI consumption, paired with experience establishing guardrails for data security, AI safety, and compliance.
• SRE & DevOps Mindset: Deep familiarity with Site Reliability Engineering (SRE) principles, automated testing, IaC, and data pipeline oversight (e.g., Snowflake/ETL validation).