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
