Senior Agentic AI Engineer

In this role you build and operate production-grade agentic AI systems for enterprise clients, collaborating in a small, senior delivery team. You shape architecture, patterns, and governance to deliver observable, reliable AI solutions. The position emphasizes hands-on engineering, fast prototyping, and contributing to reusable patterns and standards. The work is client-facing within a consultancy, with a strong delivery and impact focus.

• excellent salary
• bonus
• full benefits
• remote-first with occasional trips to office and clients

• Design and implement LLM applications with orchestration, prompts, and evaluation loops
• Build multi-agent systems including planning, tool use, safety constraints, and monitoring
• Create retrieval pipelines with embeddings, vector stores, RAG, and semantic search
• Apply knowledge modelling (ontologies, knowledge graphs) to improve reasoning and data alignment
• Integrate AI systems with enterprise platforms via APIs and connectors
• Define technical direction and client solution architecture
• Translate ambiguous needs into practical engineering approaches
• Develop lightweight technical docs, diagrams, and decision records
• Implement evaluation frameworks and safety checks for model and agent behavior
• Establish monitoring, logging, tracing, and incident management patterns
• Ensure governance, risk, and compliance in AI deployments
• Own reliability, reproducibility, and performance benchmarks
• Create reusable agent patterns, templates, and starter kits
• Share research and techniques with colleagues
• Coach peers and influence coding standards and architectural practice

• 6 to 8 years in software or AI engineering
• Hands-on experience with LLMs, embeddings, RAG, and vector stores
• Production-grade Python experience
• Experience with at least one cloud AI ecosystem (Azure OpenAI, Google Vertex AI, AWS Bedrock, or Anthropic)
• Knowledge of semantic modelling, ontologies, or knowledge graphs
• Ability to rapidly prototype from open-ended problems
• Client-facing delivery experience, ideally in a consultancy

• self-starting and entrepreneurial
• clear communication and ability to simplify complex systems
• collaborative and low-ego
• LLMs, embeddings, RAG, retrieval stacks
• multi-agent systems and agent orchestration
• prompt engineering and evaluation loops