🇳🇱 Zaandam, Netherlands · 1d ago
AI Architect
BULLIT MANAGEMENT SERVICES LIMITED
LinkedInseniorEnglish-friendly
Job Description:Role summaryDesign, build and deploy production-grade AI solutions on Microsoft Azure with a strong focus on voice AI and customer care transformationLead hands-on development of conversational and agentic AI assistants that handle live customer interactions across voice, chat and digital channelsOperate as the senior onsite technical anchor, working directly with client business, contact-centre and IT stakeholdersKey responsibilitiesDesign and develop GenAI and agentic AI solutions using Azure AI Foundry, Azure OpenAI and Azure AI servicesBuild voice-enabled AI assistants using Azure AI Speech (speech-to-text, text-to-speech, custom neural voice) and real-time streaming audio pipelinesDevelop customer care use cases such as call deflection, intent detection, live agent assist, call summarisation, post-call analytics, QA scoring and sentiment analysisIntegrate AI solutions with contact-centre platforms (Azure Communication Services, Dynamics 365 Customer Service, Genesys, Avaya, NICE, Amazon Connect or equivalent), IVR and CRM systemsImplement RAG patterns over knowledge bases using Azure AI Search, embeddings and vector stores for accurate, grounded responsesBuild multi-agent orchestration flows with tool-calling, handoff-to-human logic and fallback handlingDevelop APIs, microservices and event-driven pipelines to operationalise AI workloadsDefine and implement evaluation, guardrails and Responsible AI controls — groundedness, content safety, PII redaction, bias and hallucination checksOwn latency, scalability, security and cost optimisation for real-time voice workloadsSet up CI/CD, LLMOps and observability for AI applications using Azure DevOps / GitHubMentor developers, drive code and design reviews, and enforce engineering best practicesLead solution walkthroughs, demos and technical discussions with client stakeholdersRequired technical skillsAzure AI Foundry — projects, agent service, model catalogue, prompt flow, evaluations and deploymentAzure OpenAI — GPT model families, function/tool calling, fine-tuning, prompt engineeringAzure AI Speech — real-time STT/TTS, custom speech models, custom neural voice, speaker recognition, diarisationAzure AI Search, vector databases, embeddings and hybrid retrievalConversational AI platforms — Copilot Studio, Azure Bot Service, Language Understanding / CLUAgentic frameworks — Semantic Kernel, LangGraph, AutoGen, MCP or equivalentStrong Python; REST APIs, FastAPI, microservices, async and streaming architecturesAzure platform services — Functions, App Service, AKS, API Management, Event Hub, Service Bus, Key Vault, Entra IDAzure DevOps / GitHub Actions, CI/CD, containerisation, infrastructure as codeMonitoring and observability using Azure Monitor, Application Insights and AI evaluation toolingDomain and use-case experienceProven delivery of customer care / contact-centre AI use cases in production, not just PoCsUnderstanding of contact-centre operations — AHT, FCR, CSAT, containment rate, deflection and QA metricsExperience with omnichannel journeys spanning voice, chat, email and messagingTelecom, BFSI or large enterprise customer-service environments preferredAwareness of data privacy, call recording consent, GDPR and regulatory obligations in customer interactionsBehavioural and soft skillsStrong client-facing presence and the ability to run technical discussions independently onsiteClear technical communication with both business and engineering audiencesStrong problem-solving, ownership and delivery focusAbility to mentor and guide distributed onshore–offshore teamsExperience and qualifications12+ years of overall experience in software / AI engineering4+ years hands-on with AI/ML and GenAI solution development2+ years building voice AI or conversational AI solutions for customer careDemonstrable hands-on experience with Azure AI Foundry and Azure OpenAI in productionBachelor's degree in Engineering / Computer Science; Master's preferredAzure certifications (AI-102, AZ-204 or equivalent) preferredKey deliverables / outcomesProduction-ready voice and conversational AI assistants deployed on AzureMeasurable improvement in containment, deflection, handling time and customer satisfactionReusable AI components, prompt libraries and integration acceleratorsSecure, scalable and cost-optimised AI deployments with full observabilityTechnical documentation covering HLD, LLD and integration approachesSourced from LinkedIn. Relocantly aggregates public job postings; apply on the original site.