Team Lead, AI Engineering
Listed on 2026-08-21
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Software Development
Software Architect, AI Engineer (Applied/Software), Software Project Mgr/ Lead, DevOps
Team Lead, AI Engineering
At NiCE, we don't limit our challenges. We challenge our limits. Always. We're ambitious. We're game changers. And we play to win. We set the highest standards and execute beyond them. And if you're like us, we can offer you the ultimate career opportunity that will light a fire within you.
NICE is assembling a core engineering team to build the internal AI platform that powers intelligent automation across the enterprise. As Team Lead, AI Engineering in the Orchestration AI Development team, you will lead a hands-on engineering team responsible for building the foundational AI platform capabilities that enable teams across NICE to move faster, automate intelligently, and deliver measurable business impact.
You will guide the design, delivery, and production readiness of NICE's AI architecture, including 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: you will set engineering direction, coach engineers, remove delivery barriers, and ensure platform capabilities are scalable, secure, observable, and adopted by internal teams.
This is a leadership role for a builder who remains close to the technology. You will partner closely with the Software Architect, Dev Ops, Security, Product, and business stakeholders to translate complex enterprise needs into reliable AI platform capabilities while growing a high-performing engineering team.
How will you make an impact?
You will lead the team that builds and scales the core components of NICE's AI platform, including the integration layer, agent platform, Models Gateway, RAG pipelines, prompt and evaluation systems, and developer tooling. You will balance hands-on technical leadership with team development, delivery ownership, stakeholder alignment, and operational excellence.
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, Dev Ops, Security, Product, and business stakeholders to translate complex 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 working across AI platform, full-stack development, integration, orchestration, evaluation, and production operations
• Create a strong 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 standards for execution
Guide Core AI Platform Architecture and Execution
• Guide implementation of MCP server and client libraries that connect enterprise systems to AI agents, including Atlassian, Microsoft 365, Service Now, Workday, Salesforce, and 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 Fin Ops visibility
• Oversee RAG pipeline design, including ingestion, chunking, embedding generation, metadata enrichment, hybrid search, re-ranking, context assembly, and vector index optimization
• Establish standards for prompt management, version control, environment promotion, rollback, LLM evaluation, regression testing, hallucination detection, and human-in-the-loop feedback
Drive Adoption, Governance, and Cross-Functional Impact
• Partner with internal teams to identify…
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