Senior Azure AI Developer

Job Description:

Role summary

• Design, build and deploy production-grade AI solutions on Microsoft Azure with a strong focus on voice AI and customer care transformation
• Lead hands-on development of conversational and agentic AI assistants that handle live customer interactions across voice, chat and digital channels
• Operate as the senior onsite technical anchor, working directly with client business, contact-centre and IT stakeholders

Key responsibilities

• Design and develop GenAI and agentic AI solutions using Azure AI Foundry, Azure OpenAI and Azure AI services
• Build voice-enabled AI assistants using Azure AI Speech (speech-to-text, text-to-speech, custom neural voice) and real-time streaming audio pipelines
• Develop customer care use cases such as call deflection, intent detection, live agent assist, call summarisation, post-call analytics, QA scoring and sentiment analysis
• Integrate AI solutions with contact-centre platforms (Azure Communication Services, Dynamics 365 Customer Service, Genesys, Avaya, NICE, Amazon Connect or equivalent), IVR and CRM systems
• Implement RAG patterns over knowledge bases using Azure AI Search, embeddings and vector stores for accurate, grounded responses
• Build multi-agent orchestration flows with tool-calling, handoff-to-human logic and fallback handling
• Develop APIs, microservices and event-driven pipelines to operationalise AI workloads
• Define and implement evaluation, guardrails and Responsible AI controls - groundedness, content safety, PII redaction, bias and hallucination checks
• Own latency, scalability, security and cost optimisation for real-time voice workloads
• Set up CI/CD, LLMOps and observability for AI applications using Azure DevOps / GitHub
• Mentor developers, drive code and design reviews, and enforce engineering best practices
• Lead solution walkthroughs, demos and technical discussions with client stakeholders

Required technical skills

• Azure AI Foundry - projects, agent service, model catalogue, prompt flow, evaluations and deployment
• Azure OpenAI - GPT model families, function/tool calling, fine-tuning, prompt engineering
• Azure AI Speech - real-time STT/TTS, custom speech models, custom neural voice, speaker recognition, diarisation
• Azure AI Search, vector databases, embeddings and hybrid retrieval
• Conversational AI platforms - Copilot Studio, Azure Bot Service, Language Understanding / CLU
• Agentic frameworks - Semantic Kernel, LangGraph, AutoGen, MCP or equivalent
• Strong Python; REST APIs, FastAPI, microservices, async and streaming architectures
• Azure platform services - Functions, App Service, AKS, API Management, Event Hub, Service Bus, Key Vault, Entra ID
• Azure DevOps / GitHub Actions, CI/CD, containerisation, infrastructure as code
• Monitoring and observability using Azure Monitor, Application Insights and AI evaluation tooling

Domain and use-case experience

• Proven delivery of customer care / contact-centre AI use cases in production, not just PoCs
• Understanding of contact-centre operations - AHT, FCR, CSAT, containment rate, deflection and QA metrics
• Experience with omnichannel journeys spanning voice, chat, email and messaging
• Telecom, BFSI or large enterprise customer-service environments preferred
• Awareness of data privacy, call recording consent, GDPR and regulatory obligations in customer interactions

Behavioural and soft skills

• Strong client-facing presence and the ability to run technical discussions independently onsite
• Clear technical communication with both business and engineering audiences
• Strong problem-solving, ownership and delivery focus
• Ability to mentor and guide distributed onshore offshore teams

Experience and qualifications

• 12+ years of overall experience in software / AI engineering
• 4+ years hands-on with AI/ML and GenAI solution development
• 2+ years building voice AI or conversational AI solutions for customer care
• Demonstrable hands-on experience with Azure AI Foundry and Azure OpenAI in production
• Bachelor's degree in Engineering / Computer Science; Master's preferred
• Azure certifications (AI-102, AZ-204 or equivalent) preferred

Key deliverables / outcomes

• Production-ready voice and conversational AI assistants deployed on Azure
• Measurable improvement in containment, deflection, handling time and customer satisfaction
• Reusable AI components, prompt libraries and integration accelerators
• Secure, scalable and cost-optimised AI deployments with full observability

Technical documentation covering HLD, LLD and integration approaches