Specialist Software Architect

At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you. So, What's the Role All About? We are looking for a Specialist Software Architect to lead the architectural vision, design, and evolution of our AI-driven and Conversational Solutions platform. This role demands deep hands-on expertise in Python , AWS Cloud services , AWS Knowledge Hub , and multiple Knowledge Base technologies , along with proven experience designing simplified, scalable, and resilient architectures using microservices and AWS-native services . You will play a critical role in shaping the technical foundation of our Conversational AI platform — one that handles millions of interactions per day, meets strict SLAs (99.99%), and supports zero-downtime deployments, robust disaster recovery, and enterprise-grade AI orchestration. You will be the go-to architect for proposing pragmatic, simplified designs that balance innovation with operational excellence. How Will You Make an Impact? Architecture & System Design: • Own and define the end-to-end architecture of the Conversational AI and Knowledge platform, covering AI model integration layers, data stores, knowledge bases, and external integrations. • Design enterprise-scale, cloud-native, microservices-based architectures capable of handling high concurrency, burst traffic, and real-time conversational workloads. • Propose simplified, cost-effective architectural designs leveraging AWS-native services — avoiding over-engineering while ensuring scalability and resilience. • Define reference architectures, architectural blueprints, and non-functional requirements (NFRs) for performance, reliability, security, and maintainability. • Drive event-driven and asynchronous architectures using AWS-native services such as SQS, SNS, EventBridge, and Step Functions. • Define best practices for microservices design, domain-driven design (DDD), API design (REST, async/event APIs), and AI integration patterns. • Review and guide complex application-level designs, critical code paths, and AI pipeline architectures. AI & Conversational Solution Architecture: • Architect and evolve AI-driven Conversational solutions integrating large language models (LLMs), prompt engineering patterns, and enterprise AI orchestration frameworks. • Design and implement Knowledge Base solutions using AWS Knowledge Hub , vector databases, RAG (Retrieval-Augmented Generation) architectures, and other knowledge management technologies. • Define AI model integration strategies — including model selection, fine-tuning approaches, embedding pipelines, and response quality frameworks. • Drive conversational UX architecture, ensuring seamless, context-aware, and low-latency interaction flows. • Evaluate and recommend AI tooling, platforms, and frameworks aligned with business needs (e.g., AWS Bedrock, LangChain, OpenAI, Anthropic, Kore.ai, Dialogflow). • Lead the design of multi-turn conversation management, intent resolution, and fallback handling architectures. AWS Cloud & Microservices Architecture: • Architect and optimise solutions using AWS services including but not limited to Lambda, API Gateway, SQS, SNS, Step Functions, DynamoDB, Aurora Serverless, S3, Bedrock, and Kendra. • Design cost-efficient, elastic scaling strategies aligned with unpredictable conversational AI workloads. • Lead cloud governance, environment isolation, multi-account strategy, and infrastructure-as-code (IaC) practices. • Define CI/CD pipeline standards, DevOps practices, and containerisation approaches (Docker, Kubernetes) for AI workload deployments. • Drive microservices decomposition strategies — identifying service boundaries, inter-service communication patterns, and data ownership models. Scalability, Resilience & Disaster Recovery: • Design for extreme scale — high concurrent users, high TPS, and real-time AI inferencing demands. • Own resilience strategies including multi-AZ and multi-region architectures, active-active or active-passive DR setups, and graceful degradation mechanisms. • Define and validate RPO/RTO goals, conduct chaos testing, and lead DR drills. • Ensure zero-downtime deployment capabilities through blue/green, canary, and rolling deployment strategies. Security, Compliance & Reliability: • Embed security by design across all AI and data architecture layers — covering authentication, authorisation, data privacy, encryption, and model safety guardrails. • Support compliance requirements (SOC2, ISO, GDPR) from an architectural standpoint, particularly around AI data handling and knowledge base access controls. • Partner with SRE and DevOps teams on observability, logging, distributed tracing, and alerting for AI workloads. • Define SLIs, SLOs, and capacity planning models for conversational AI…