Senior AI Engineer - Enterprise GenAI Solutions

Role : Senior AI Engineer – Enterprise GenAI Solutions Type : Casual Contract Duration : 6 months with possible extension About the Role We are looking for a hands‑on Senior AI Engineer to design and deliver enterprise‑grade Generative AI solutions. This role will focus on building scalable, secure, and practical AI‑enabled applications that support knowledge discovery, intelligent search, workflow automation, information extraction, and AI‑enabled decision support. The successful candidate will have strong Python engineering experience, practical experience building LLM‑powered applications, and a solid understanding of Retrieval‑Augmented Generation, vector search, prompt grounding, cloud‑native engineering, responsible AI, and production‑ready solution design. This is a senior engineering role requiring strong ownership, proactive technical leadership, and the ability to work independently across complex and ambiguous problem areas. Key Responsibilities Lead the design and implementation of enterprise GenAI solution components from concept through to delivery. Build and enhance AI application pipelines covering data ingestion, retrieval, grounding, response generation, evaluation, and monitoring. Design effective retrieval and search patterns across structured and unstructured information sources. Develop AI‑powered search, knowledge discovery, document intelligence, and workflow automation capabilities. Improve solution accuracy, reliability, and business relevance through retrieval design, grounding techniques, and quality controls. Work closely with architects, developers, analysts, and business stakeholders to translate complex business requirements into practical AI engineering solutions. Support secure and scalable integration of AI capabilities into enterprise technology environments. Apply responsible AI principles including privacy, security, transparency, auditability, fallback behaviour, and human oversight. Troubleshoot complex AI application issues including retrieval gaps, poor context selection, inconsistent outputs, and response quality concerns. Contribute to production readiness including performance, scalability, monitoring, maintainability, and operational support. Help define reusable engineering patterns that can be applied across multiple enterprise AI use cases. Provide technical leadership, mentoring, and guidance to improve overall engineering quality. Required Skills and Experience 8+ years of overall technology experience. 4+ years of experience in AI/ML, GenAI, data engineering, cloud‑native engineering, intelligent automation, or related areas. Strong hands‑on experience in Python for backend, AI, automation, data engineering, or cloud‑native workloads. Proven experience designing and delivering LLM‑powered or GenAI applications in enterprise environments. Strong understanding of Retrieval‑Augmented Generation, embeddings, vector search, prompt grounding, and response generation. Experience working with structured and unstructured data sources including documents, metadata, business artefacts, or operational information. Experience designing AI solutions that are secure, auditable, explainable, and suitable for enterprise use. Familiarity with cloud‑native application design and modern engineering practices. Ability to troubleshoot complex AI application quality issues and identify practical remediation options. Strong ownership mindset with the ability to work independently and lead problem‑solving without detailed task‑level direction. Excellent communication skills with the ability to engage both technical and non‑technical stakeholders. Preferred Skills Experience with GraphRAG, knowledge graphs, relationship modelling, or advanced knowledge discovery patterns. Experience with AI evaluation, retrieval quality assessment, confidence scoring, feedback loops, or response quality tuning. Experience with document understanding, information extraction, intelligent search, or workflow automation. Exposure to source‑code analysis, dependency mapping, application intelligence, or technical metadata analysis. Experience with data mapping, data migration support, lineage, transformation rules, or metadata‑driven analysis. Experience designing AI‑enabled workflows that include review, validation, exception handling, or auditability. Experience working in regulated, enterprise, or large‑scale technology environments. Familiarity with one or more major cloud platforms and common cloud‑native AI architecture patterns. Frontend exposure for AI‑enabled applications would be beneficial but is not essential. The successful candidate should be able to operate as a senior technical owner, not simply as a developer working through assigned tasks. They should be comfortable working with ambiguity, identifying gaps and risks, proposing practical technical options, and driving implementation independently. The role requires someone who can balance innovation with security, governance, responsible AI, and production readiness. Key Technologies and Concepts Python, Generative AI, Large Language Models, RAG, GraphRAG, Vector Search, Embeddings, Prompt Grounding, Knowledge Graphs, Document Ingestion, AI Guardrails, Human‑in‑the‑Loop Workflows, Responsible AI, Cloud‑Native Engineering, Source‑Code Analysis, Data Mapping, Workflow Automation. #J-18808-Ljbffr