The Role Purpose:
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.
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.
The successful candidate will play a key role in building scalable systems for external market-facing products.
Your Responsibilities will include:
- Design, develop, test, and deploy end-to-end GenAI-enabled software solutions (services, APIs, workflows, and product features).
- Build agentic systems, including multi-agent architectures, tool-use patterns, orchestration flows, and production tooling integrations.
- 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.
- Develop and operationalize robust prompt and context engineering practices (prompt templating, context window management, instruction hierarchy, tool routing, and response formatting).
- Implement agent memory management patterns and frameworks to support short-term and long-term memory, personalization, and session continuity (where applicable).
- 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.
- 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.
- Design and maintain data stores supporting GenAI applications, including relational, vector, and graph patterns to enable retrieval, reasoning, and relationship-aware experiences.
- Implement AI Governance practices: apply and monitor guardrails (policy enforcement, content filtering, PII handling, prompt injection defences, auditability, and safe tool execution).
- Evaluation and monitoring approaches for GenAI systems (quality, grounding, safety, latency, cost), contributing to continuous improvement initiatives.
- Collaborate with cross-functional teams (Product, Engineering, UX, Data/ML, Security, Compliance) to translate business requirements into technically sound solutions.
- Participate in code reviews, architectural discussions, and agile planning sessions; contribute to internal standards, patterns, and reusable components.
- Maintain and enhance legacy systems where required, integrating GenAI functionality safely without compromising stability.
The ideal candidate for the role will have the following qualifications, experience and knowledge:
Educational Background:
- Bachelor’s degree in computer science, Information Technology, Data Science, Artificial Intelligence, Software Engineering, or equivalent
- Postgraduate qualification in Artificial Intelligence, Machine Learning, Data Science, or Applied Mathematics is advantageous
- Relevant certifications are advantageous (examples include Microsoft Azure AI Engineer, AWS Machine Learning, or similar cloud/AI certifications).
Work Experience:
- 3-4 years of experience in delivering production-grade software (greenfield and brownfield), including backend services and customer-facing modules.
- Proven hands-on experience building and deploying GenAI solutions in production, including LLM-powered features, RAG-based systems, or agentic workflows.
- Experience implementing governance controls and operational monitoring for GenAI systems in real-world environments.
- Strong practical exposure to modern software engineering practices: CI/CD, testing, code review, observability, and secure API design.
Knowledge:
- Strong understanding of LLM/embedding fundamentals as applied in production systems (retrieval, grounding, context shaping, evaluation, and failure modes).
- Knowledge of multi-agent patterns, tool/function calling (MCP), workflow orchestration, and safe execution boundaries.
- Understanding of data management strategies for GenAI (document pipelines, vector search, graph relationships, and relational integrity).
- Familiarity with data privacy principles, security-by-design, and governance expectations relevant to enterprise-grade AI solutions.
Technical Skills:
Core Engineering & Platforms
- Python (GenAI services, orchestration, data pipelines), C#, REST APIs, microservices, event-driven systems (Kafka).
- Strong engineering fundamentals (clean architecture, testing, security, performance).
GenAI, Agents & RAG
- Prompt and context engineering, agent frameworks (e.g. LangChain, LangGraph, LangSmith, CrewAI, Semantic Kernel), workflow automation (e.g. n8n).
- 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.
Data, Memory & Storage
- PostgreSQL (incl. timescale), vector DBs (Qdrant, Milvus), graph DBs (Neo4j, Apache AGE).
- Agent memory patterns (session, long-term, summarization) with privacy and risk controls.
Security, Governance & Ops
- GenAI guardrails (prompt/tool injection defence, PII handling, auditing).
- Cloud & DevOps (Azure/AWS, CI/CD, Git, Docker/Kubernetes). Observability for LLM systems. Agile delivery and GenAI-specific testing/evaluation
