AI Platform Delivery Lead Louisville - KY - Kentucky

JD:

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 AIAIOps 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 (Google Cloud Platform, AWS), and CICD 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., SnowflakeETL validation).

Roles & Responsibilities

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.

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.

Generic Managerial Skills, If any

Should be able to work with Client directly as per the requirements at Onsite

Client engagement, leadership, global delivery, stakeholder management, thought leadership,

reporting & governance

Strong leadership and decision-making abilities

Excellent communication and executive presence

Ability to manage complex, high-visibility programs

Experience working in global delivery models

Key Words to search in Resume

Google Cloud Platform, AIOps, Automation, GCVE, Hybrid Cloud, AIML, LLM, Power Shell, Ansible, Shell Script

Pre-Screening Questionnaire

Explain an Agentic AI architecture you have designed or implemented. What problem did it solve?

What is the difference between traditional AI workflows and Agentic AI systems?

How have you used LLMs in production environments?

What is RAG (Retrieval-Augmented Generation), and where have you applied it?

What experience do you have with MCP (Model Context Protocol) or tool-calling architectures?

Role Descriptions: AI Ops lead

Essential Skills: AI Ops lead

Desirable Skills:

Keyword:

Skills: Digital : AnsibleDigital: Terraform

Experience Required: 8-10