Our client are a private equity backed firm who are looking for a deep AI/Agentic Engineer to lead the design and delivery of the organisations complex AI-native engagements, from agentic systems to semantic layers. Pair hands-on technical leadership with client trust, shaping both enterprise outcomes and the firm’s long-term engineering capability.
Responsibilities:
You will design and build production-grade AI systems across:
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LLM applications using modern orchestration patterns, prompt frameworks, and evaluation loops.
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Multi-agent architectures, including planning, delegation, safety constraints, and monitoring.
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Retrieval & vector-based systems, embeddings, structured reasoning, and semantic workflows.
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Ontology & knowledge modelling literacy, enabling more precise reasoning and data alignment.
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Integrations & automation, including API workflows, tools, and enterprise connectors.
Skills: -
6–8 years in Software/ application engineering, AI engineering, or applied data engineering.
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Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores.
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Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns.
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Proficiency in Python, including ability to build production-grade workflows.
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Experience with at least one cloud AI ecosystem (Azure/OpenAI, GCP/Vertex, Anthropic, AWS etc.).
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Familiarity with semantic modelling, ontologies, or knowledge graph thinking - literacy required, mastery a bonus.
