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.

Team Lead, AI Engineering

So, what’s the role all about?

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, DevOps, 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, DevOps, 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, ServiceNow, 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 FinOps 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 high-value AI use cases and convert them into reusable platform capabilities, SDKs, patterns, and documentation

Define governance practices that support responsible AI development, secure enterprise integration, cost transparency, and compliant use of internal data

Measure platform adoption, reliability, developer productivity, operational efficiency, and business impact through clear dashboards and success metrics

Ensure Production Excellence and Continuous Improvement

Have you got what it takes?

7+ years of professional software engineering experience, including hands-on experience with Python, TypeScript, or similar languages in production environments

2+ years of technical leadership, team leadership, or engineering management experience, with a track record of mentoring engineers and driving delivery outcomes

Hands-on experience building and deploying LLM-powered applications, including RAG pipelines, agents, tool use, prompt engineering systems, or evaluation frameworks

Strong understanding of REST API design, async programming, distributed systems, event-driven architecture, and production engineering practices

Experience with at least one agent or orchestration framework such as LangChain , LangGraph , AutoGen , CrewAI , or equivalent

Practical knowledge of Azure services, including Azure OpenAI, Azure Storage, Azure AI Search, Azure Container Apps, AKS, or related cloud-native services

Demonstrated ability to set engineering standards for code quality, testing, documentation, security, observability, and CI/CD

Strong communication and stakeholder management skills, with the ability to translate technical complexity into clear business impact

Ability to lead through ambiguity, prioritize effectively, and keep teams aligned in a fast-moving environment with evolving requirements

Bonus:

Experience implementing MCP servers, clients, or similar tool-integration protocols at enterprise scale

Familiarity with Anthropic Claude API, Azure AI Foundry, Azure OpenAI, tool use patterns, and multi-turn conversation management

Experience with vector databases such as Azure AI Search, pgvector , Qdrant , Weaviate , or Pinecone

Knowledge of LLM evaluation frameworks such as Evals, RAGAS, LangSmith , or custom harness development

Experience leading developer platform , internal tools, AI platform , or infrastructure engineering teams

Background in enterprise IT systems integration, including Jira, ServiceNow, Salesforce, Workday, Microsoft 365, or Snowflake

Familiarity with OpenTelemetry instrumentation, Azure Monitor, Grafana, FinOps, and production reliability practices

Why Join Us?

At NiCE , we don’t just connect systems, we connect people, platforms, and possibilities. In this role, you’ll lead a team at the center of NiCE’s internal AI transformation, building the platform foundations that help teams automate work, improve developer productivity, and scale AI responsibly across the enterprise.

If you are a technical leader, hands-on builder, and collaborative people manager who is excited to shape how AI is engineered, governed, and adopted at enterprise scale, this is your opportunity to build the future of work with NiCE .

What’s in it for you?

Join an ever-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr!

Enjoy NiCE-FLEX!

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