Role Overview

Key Responsibilities

Agentic AI Engineering

• Independently design, develop, test and deploy agentic AI solutions that can reason, plan, invoke tools and execute multi-step enterprise workflows.
• Build single- and multi-agent solutions incorporating LLMs, retrieval-augmented generation (RAG), memory, tool/function calling, workflow orchestration and human-in-the-loop controls.
• Develop reusable agent components, APIs, connectors and orchestration patterns that can be applied across enterprise use cases.
• Integrate AI agents with enterprise applications, databases, knowledge repositories and APIs.

Machine Learning & AI Development

• Develop and productionise machine learning and generative AI solutions using appropriate models, frameworks and cloud AI services.
• Evaluate and select models based on accuracy, latency, cost, security and business requirements.
• Design RAG pipelines covering document ingestion, chunking, embeddings, retrieval, reranking and grounding.
• Establish systematic evaluation approaches for AI solutions, including task completion, answer quality, groundedness, hallucination, latency and cost.

Enterprise AI Engineering

• Translate business processes and workflow requirements into scalable AI solution architectures.
• Build AI applications using modern frameworks and technologies such as Python, REST APIs, LLM APIs, vector databases and agent orchestration frameworks.
• Implement appropriate authentication, authorization, observability, auditability, guardrails and human approval mechanisms for enterprise AI agents.
• Design solutions that operate reliably within enterprise security, data governance and responsible AI requirements.

Productionisation & Operations

• Apply software engineering practices including source control, automated testing, CI/CD, containerisation and API design.
• Deploy and operate AI workloads in cloud or enterprise environments.
• Implement monitoring and evaluation to identify model degradation, failed agent executions, hallucinations, excessive token consumption and workflow exceptions.
• Troubleshoot AI applications across models, prompts, retrieval pipelines, APIs and downstream enterprise systems.

Business Partnership

• Work directly with business and technical stakeholders to identify workflows suitable for AI-enabled automation.
• Rapidly develop prototypes and evolve successful prototypes into production solutions.
• Clearly communicate AI capabilities, limitations, risks and technical trade-offs to both technical and non-technical stakeholders.

Key Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related discipline.
• 5–7 years of relevant hands-on experience in machine learning, AI engineering, software engineering or AI application development.
• Demonstrated experience taking ML/AI applications from prototype through production deployment.
• Proven ability to independently architect and develop agentic AI solutions for enterprise workflows, rather than relying solely on low-code or pre-built chatbot platforms.
• Strong programming capability in Python, with solid software engineering fundamentals.
• Hands-on experience with LLMs, prompt engineering, RAG, embeddings, vector search, tool/function calling and agentic workflows.
• Experience integrating AI solutions with REST APIs, databases and enterprise applications.
• Experience with at least one major cloud platform and its AI/ML services.
• Strong understanding of production considerations including security, scalability, observability, testing, performance and cost optimisation.

Good To Have

• Hands-on experience with agentic/orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen or equivalent frameworks.
• Experience with enterprise AI platforms and model services such as Azure AI Foundry / Azure OpenAI or equivalent platforms.
• Experience with Docker, Kubernetes, CI/CD and MLOps/LLMOps practices.
• Experience designing human-in-the-loop, approval and exception-handling patterns for autonomous or semi-autonomous agents.
• Familiarity with model evaluation, AI guardrails, responsible AI, security and AI governance.
• Experience delivering AI solutions in complex enterprise environments involving multiple systems and stakeholder groups.