Full Stack AI & LLM Engineer

JD : Mandatory AI & LLM Experience • Proven experience delivering production-grade LLM solutions. • Demonstrated success deploying AI solutions that generated measurable business outcomes. • Hands-on experience with Large Language Models such as OpenAI, Claude, Gemini, Llama or equivalent. • Hands-on experience with prompt engineering, Retrieval Augmented Generation (RAG), AI agents and agentic workflows. • Experience with model orchestration frameworks, AI evaluation frameworks and model performance optimization. • Experience integrating LLMs into enterprise applications and business workflows. • Understanding of AI security, privacy, explainability, fairness, governance and responsible AI controls. Mandatory Intelligent Document Processing Experience • Deep experience delivering Intelligent Document Processing solutions. • Experience with document classification, data extraction, OCR technologies, validation and verification frameworks, document workflow automation, redaction services and document intelligence platforms. • Demonstrated ability to improve customer and operational outcomes through intelligent automation. • Experience processing structured, semi-structured and unstructured document workloads. Technical Engineering Experience • Extensive experience developing enterprise applications. • Strong programming capability in one or more of Python, Java, Go or .NET. • Strong ReactJS development experience. • Strong API design and integration experience. • Strong AWS cloud engineering capability. • Docker and containerization experience. • CI/CD pipeline design and implementation experience. • Automated testing framework experience. • Experience working in highly regulated enterprise environments. Engineering Excellence • Strong software design and solution architecture skills. • Deep understanding of DevSecOps principles. • Experience designing resilient and observable systems. • Strong problem-solving and systems-thinking capabilities. • Demonstrated ability to balance innovation, delivery, risk and resilience. Key Responsibilities : AI, LLM & Document Intelligence • Design, build and deploy AI-powered solutions into production environments. • Develop and maintain LLM-based applications that deliver measurable business value. • Design and implement Retrieval Augmented Generation (RAG) architectures. • Build AI agents and orchestration frameworks that automate document-centric business processes. • Develop prompt engineering, model evaluation and model optimisation capabilities. • Assess and select appropriate AI models based on business outcomes, performance, cost and risk considerations. • Drive adoption of AI-powered engineering practices across the crew. Intelligent Document Processing • Design and build enterprise-scale Intelligent Document Processing solutions. • Develop document classification, extraction, validation, redaction and workflow automation capabilities. • Integrate OCR, document intelligence and AI technologies into customer journeys. • Improve straight-through processing rates through intelligent automation. • Build reusable document intelligence services that can be leveraged across multiple business domains. • Drive innovation in document processing through emerging AI technologies. Full Stack Engineering • Design, develop and maintain modern web applications and APIs. • Build responsive user experiences using ReactJS. • Develop backend services using Python, Java, Go or .NET. • Design scalable microservices and event-driven architectures. • Contribute to technical architecture, engineering standards and strategic technology direction. • Mentor and uplift engineering capability across the squad and broader engineering community. Cloud, Platform & DevSecOps • Build and operate cloud-native solutions on AWS. • Develop containerised workloads using Docker. • Design and enhance CI/CD pipelines to improve delivery speed and quality. • Automate infrastructure, testing and deployment activities wherever possible. • Improve observability, operational resilience and platform reliability. • Apply modern DevSecOps practices throughout the software delivery lifecycle.