AI Engineer

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    "description": "<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">The Role Purpose:</span></span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">We are seeking an AI Engineer to join our team, with a primary focus on designing, developing, and maintaining production-grade software solutions that leverage Large Language Models (LLMs), embedding models, and other generative technologies. This role emphasizes building scalable, reliable, and secure agentic solutions (including multi-agent systems) for external market-facing products and internal enterprise enablement.</span></p>\n<p><br></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">The successful candidate will combine strong software engineering fundamentals with deep practical capability in retrieval-augmented generation (RAG), knowledge management, prompt/context engineering, model/tool orchestration, and AI governance guardrails.</span></p>\n<p><br></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">The successful candidate will play a key role in building scalable systems for external market-facing products.</span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Your Responsibilities will include:</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Design, develop, test, and deploy end-to-end GenAI-enabled software solutions (services, APIs, workflows, and product features).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Build agentic systems, including multi-agent architectures, tool-use patterns, orchestration flows, and production tooling integrations.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Design and implement RAG pipelines for both product and enterprise contexts, including knowledge-based curation, ingestion, document processing, chunking strategies, embedding generation, retrieval tuning, and answer grounding.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Develop and operationalize robust prompt and context engineering practices (prompt templating, context window management, instruction hierarchy, tool routing, and response formatting).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Implement agent memory management patterns and frameworks to support short-term and long-term memory, personalization, and session continuity (where applicable).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Integrate and operate model providers and runtimes for production use-cases, including hosted APIs and self-hosted inference, optimizing for latency, cost, throughput, and reliability.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Develop microservices and APIs that expose GenAI/agent capabilities to web applications and downstream systems; maintain strong engineering standards for versioning, observability, and backward compatibility.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Design and maintain data stores supporting GenAI applications, including relational, vector, and graph patterns to enable retrieval, reasoning, and relationship-aware experiences.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Implement AI Governance practices: apply and monitor guardrails (policy enforcement, content filtering, PII handling, prompt injection defences, auditability, and safe tool execution).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Evaluation and monitoring approaches for GenAI systems (quality, grounding, safety, latency, cost), contributing to continuous improvement initiatives.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Collaborate with cross-functional teams (Product, Engineering, UX, Data/ML, Security, Compliance) to translate business requirements into technically sound solutions.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Participate in code reviews, architectural discussions, and agile planning sessions; contribute to internal standards, patterns, and reusable components.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Maintain and enhance legacy systems where required, integrating GenAI functionality safely without compromising stability.</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">The ideal candidate for the role will have the following qualifications, experience and knowledge:</span></span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Educational Background:</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Bachelor’s degree in computer science, Information Technology, Data Science, Artificial Intelligence, Software Engineering, or equivalent</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Postgraduate qualification in Artificial Intelligence, Machine Learning, Data Science, or Applied Mathematics is advantageous</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Relevant certifications are advantageous (examples include Microsoft Azure AI Engineer, AWS Machine Learning, or similar cloud/AI certifications).</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Work Experience:</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">1-3 years of experience in delivering production-grade software (greenfield and brownfield), including backend services and customer-facing modules.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Proven hands-on experience building and deploying GenAI solutions in production, including LLM-powered features, RAG-based systems, or agentic workflows.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Experience implementing governance controls and operational monitoring for GenAI systems in real-world environments.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Strong practical exposure to modern software engineering practices: CI/CD, testing, code review, observability, and secure API design.</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Knowledge:</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Strong understanding of LLM/embedding fundamentals as applied in production systems (retrieval, grounding, context shaping, evaluation, and failure modes).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Knowledge of multi-agent patterns, tool/function calling (MCP), workflow orchestration, and safe execution boundaries.</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Understanding of data management strategies for GenAI (document pipelines, vector search, graph relationships, and relational integrity).</span></li>\n<li><span style=\"font-size: 10pt\">Familiarity with data privacy principles, security-by-design, and governance expectations relevant to enterprise-grade AI solutions.</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"> </span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Technical Skills:</span></span></p>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Core Engineering &amp; Platforms</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Python (GenAI services, orchestration, data pipelines), C#, REST APIs, microservices, event-driven systems (Kafka).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Strong engineering fundamentals (clean architecture, testing, security, performance).</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">GenAI, Agents &amp; RAG</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Prompt and context engineering, agent frameworks (e.g. LangChain, LangGraph, LangSmith, CrewAI, Semantic Kernel), workflow automation (e.g. n8n).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Experience with hosted and self-hosted models (OpenAI/Azure/AWS, Ollama, vLLM). RAG systems: document ingestion, embeddings, hybrid retrieval, reranking, citations, and knowledge lifecycle management.</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Data, Memory &amp; Storage</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">PostgreSQL (incl. timescale), vector DBs (Qdrant, Milvus), graph DBs (Neo4j, Apache AGE).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Agent memory patterns (session, long-term, summarization) with privacy and risk controls.</span></li>\n</ul>\n<p><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\"><span style=\"font-weight: bold\">Security, Governance &amp; Ops</span></span></p>\n<ul>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">GenAI guardrails (prompt/tool injection defence, PII handling, auditing).</span></li>\n<li><span style=\"color: inherit; font-size: 10pt; font-weight: inherit\">Cloud &amp; DevOps (Azure/AWS, CI/CD, Git, Docker/Kubernetes). Observability for LLM systems. Agile delivery and GenAI-specific testing/evaluation</span></li>\n</ul>",
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