Team Lead, AI Engineering
NICE · USA - Atlanta, GA; USA - Hoboken, NJ; USA - Sandy, UT
About this role
**Team Lead, AI Engineering (NiCE)** NiCE is assembling a core engineering team to build an internal AI platform that powers intelligent automation across the enterprise. As **Team Lead, AI Engineering** in the **Orchestration AI Development** team, you’ll lead a hands-on engineering team building the foundational AI platform capabilities that help teams move faster, automate intelligently, and deliver measurable business impact. You’ll guide the design, delivery, and production readiness of NICE’s AI architecture—covering the **MCP integration layer**, **agent orchestration engine**, **Models Gateway**, **RAG pipelines**, **prompt management**, **LLM evaluation**, and **developer tooling**. This role requires both technical depth and people leadership: set engineering direction, coach engineers, remove delivery barriers, and ensure the platform is scalable, secure, observable, and adopted by internal teams. You’ll partner closely with the **Software Architect, DevOps, Security, Product, and business stakeholders** to translate complex enterprise needs into reliable AI platform capabilities while growing a high-performing engineering team. --- ## How you will make an impact ### Lead Platform Engineering Delivery - Lead the engineering roadmap and delivery execution for core AI platform capabilities, ensuring priorities are clear, sequenced, and aligned to business outcomes. - Partner with architecture, DevOps, Security, Product, and business stakeholders to translate requirements into scalable technical plans. - Own delivery quality across releases, including code review standards, test coverage, production readiness, operational runbooks, and rollback plans. ### Build and Develop a High-Performing AI Engineering Team - Lead, mentor, and grow engineers across AI platform, full-stack development, integration, orchestration, evaluation, and production operations. - Create an engineering culture focused on ownership, technical excellence, learning, collaboration, and pragmatic delivery. - Coach team members through technical decisions, design reviews, incident learnings, and career development while maintaining high execution standards. ### Guide Core AI Platform Architecture and Execution - Guide implementation of **MCP server/client libraries** connecting enterprise systems to AI agents (e.g., Atlassian, Microsoft 365, ServiceNow, Workday, Salesforce, Snowflake). - Lead delivery of **agent orchestration** capabilities, including **ReAct loops**, tool-augmented reasoning, multi-agent workflows, memory/state management, and A2A interoperability. - Ensure technical designs address security, authentication, reliability, performance, observability, and long-term maintainability. ### Scale Models Gateway, RAG, and Evaluation Capabilities - Lead development of the **Models Gateway**, including provider abstraction, model routing, fallback chains, cost-based dispatch, latency budgeting, quota enforcement, and FinOps visibility. - Oversee **RAG pipeline** design: ingestion, chunking, embedding generation, metadata enrichment, hybrid search, re-ranking, context assembly, and vector index optimization. - Establish standards for prompt management, evaluation, and continuous improvement of model performance.
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