Team Lead, AI Engineering

NICE•Atlanta, GA

11d•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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 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
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
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