Hiring: Software Engineer, AI Company: NiCE Location: Sandy, UT Job Posted Time: 2026-09-10 17:59:02 Employment Type: Full-time Target Skills & Keywords: AI Platform, MCP Integration, Agent Orchestration, Models Gateway, RAG Pipelines, Vector Databases, Azure AI, LLM, Full-Stack Engineering, Python, OAuth2, Azure OpenAI, Anthropic Claude, Azure AI Search, Prompt Management, LLM Evals, CI/CD Experience: - Full-stack engineering role with a strong AI focus - Hands-on experience building production-grade AI systems and platforms - Experience implementing AI agent frameworks (ReAct loops, tool-augmented reasoning, multi-agent orchestration, memory and state management) - Experience integrating with LLM providers such as Azure AI Foundry and Anthropic Claude API - Experience with vector databases and retrieval pipelines - Significant autonomy on technically complex problems Required Skills: - Build MCP server and client libraries connecting enterprise systems (Atlassian, Microsoft 365, ServiceNow, Workday, Salesforce, Snowflake) to AI agents - Design and expose clean tool schemas; handle auth flows (OAuth2, managed identity); implement error handling, retries, and rate limiting - Build A2A (Agent-to-Agent) interoperability layer for multi-agent collaboration - Implement production-grade AI agent frameworks: ReAct loops, tool-augmented reasoning, multi-agent orchestration, memory and state management - Build agent harnesses for IT helpdesk automation, procurement workflows, HR self-service, developer productivity agents - Integrate with Azure AI Foundry and Anthropic Claude API, managing context windows, tool use, streaming responses, and multi-turn conversations - Build unified gateway abstracting multiple LLM providers (Azure OpenAI, Anthropic, open-source models via Azure ML) - Implement model routing logic, fallback chains, cost-based dispatch, latency budgeting, and per-team quota enforcement - Add logging, token metering, and usage dashboards for FinOps visibility - Design and implement document ingestion pipelines: chunking, embedding generation, metadata enrichment, and upsert into vector stores - Build retrieval pipelines with hybrid search (dense + sparse), re-ranking, and context assembly for LLM prompts - Manage vector DB infrastructure on Azure AI Search; own schema design and index optimization - Build prompt registry: version control, templating engine, environment promotion, and rollback - Design and run LLM evaluation pipelines: automated regression tests, hallucination detection, task-specific benchmarks - Implement human-in-the-loop feedback collection and model performance tracking dashboards - Contribute to developer tooling and CI/CD Qualifications: - Strong full-stack engineering background with AI focus - Ability to write clean, production-quality code - Experience collaborating with Software Architects, DevOps, and Security teams - Experience building and shipping production AI systems used at scale - Familiarity with enterprise system integrations and AI orchestration Compensation: Not specified Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don’t miss this opportunity to join a forward-thinking team!