AI Delivery Lead: Enterprise Platforms & Automation

SmartRecruiters, Inc.

Northern (KY)

Hybrid

USD 180,000 - 240,000

Full time

27 hours ago

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Benefits offered by this job

Medical, Dental, Vision

401(k)

Employer-paid Life Insurance

Health Savings Account

Flexible Spending Accounts

Job summary

Endava is seeking a deeply technical Technical Delivery Lead to drive AI, Agentic AI, and infrastructure automation initiatives globally. You will lead architecture, software engineering, and delivery, while hands-on coding and design decisions shape the stack.

The role requires strong expertise in Python, MCP servers, LangChain/CrewAI, and cloud platforms (AWS/GCP). You will coordinate cross-functional teams across AI/ML, cloud, SRE, security, and data pipelines.

Qualifications

8+ years of experience leading complex technical projects in a global enterprise IT environment.
2+ years leading AI/AI-driven infrastructure deployments.
Strong hands-on knowledge of Python and asynchronous programming.
Hands-on experience developing or integrating MCP servers and tool calling architectures.
Deep understanding of LLMs, Agentic AI, multi-agent reasoning, tool calling, RAG, and AI orchestration frameworks such as LangChain or CrewAI.
Experience with Dynatrace/Davis AI, observability, AIOps, or self-healing infrastructure automation.
Hands-on experience with AWS and/or GCP.
Experience with CI/CD, IaC, automated testing, and SRE practices.
Experience with data platforms, Snowflake, ETL, and data validation.

Responsibilities

Lead end-to-end delivery of enterprise AI, Agentic AI, AIOps, observability, and infrastructure automation initiatives.
Drive development and deployment of Agentic AI platforms, multi-agent solutions, MCP-based architectures, and automated self-healing infrastructure.
Provide hands-on technical leadership, including Python development and code reviews.
Orchestrate cross-functional teams across AI/ML, cloud, infrastructure, SRE, security, and application engineering.
Translate complex IT Infrastructure & Operations environments into schemas for AI agents.
Develop automation for incident remediation, vulnerability management, observability, and runbook execution.
Work with enterprise observability platforms such as Dynatrace/Davis AI, Splunk, and CloudWatch.
Establish guardrails for AI safety, data security, compliance, and access controls.
Drive CI/CD, IaC, automated testing, and cloud deployment practices.
Provide oversight of data pipelines including ETL and Snowflake validation.
Set engineering standards for AI agents, MCP servers, and automation frameworks.
Track milestones, risks, outcomes, and financial benefits for executive visibility.