Staffworx Limited
Senior Forward Deployed Engineer LLM, RAG, Agentic AI and Prompt Engineering | Permanent | Remote first, UK A fast-growing, engineering-led AI consultancy is hiring Lead and Senior Forward Deployed Engineers on a remote-first basis across the UK. You will lead the design and delivery of complex client engagements spanning agentic systems, retrieval architectures and semantic layers, working directly inside enterprise client teams. Client demand for AI-led transformation is accelerating and our client is expanding its permanent engineering team across agentic systems, retrieval, ontologies and AI-enabled operations. As a Forward Deployed Engineer, you will work closely with the client, in their workshops and inside their systems, turning real operational problems into working AI solutions. These are senior, hands-on consulting roles that combine deep engineering craft with commercial and product thinking. You will work with senior client stakeholders alongside consulting and delivery colleagues, and lead technical design on your engagements. Prompt design, evaluation and guardrails are central to how you build, so the agents you ship are safe, reliable and measurable once they are live. You will shape both client outcomes and the firm's long-term AI engineering capability, while staying hands-on in the code. What you'll do Embedded client delivery and technical leadership Lead technical design within client engagements and set architectural direction across delivery teams Work alongside client teams, remotely and on occasional site visits, to understand their workflows, data and constraints first hand Turn ambiguous client problems into clear, feasible engineering approaches and fast prototypes for client validation Assess client data, platforms, security constraints and operating models Contribute to framing, use-case shaping and technical scoping discussions Work with client domain experts to surface edge cases and operational realities Produce clear, lightweight technical artefacts for client and internal audiences Act as the trusted technical voice on the engagement, from first workshop to go-live Agentic AI, LLM and RAG engineering Design and build LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops Build multi-agent architectures covering planning, delegation, tool use, memory, safety constraints and monitoring Build retrieval and vector-based systems: embeddings, RAG, structured reasoning and semantic workflows Connect agents to enterprise platforms through APIs, tools, MCP Servers and connectors Apply ontologies or knowledge graphs where they improve reasoning and data alignment Prompt engineering and evaluation Design, test and version system prompts, tool descriptions and agent instructions Build reusable prompt libraries and patterns that hold up across clients and use cases Create evaluation sets and automated eval loops to measure accuracy, safety and consistency Implement guardrails, structured outputs and explainability checks Diagnose and fix failure modes such as hallucination, tool misuse and prompt injection Delivery excellence, AI operations and reliability Implement evaluation frameworks and safety checks across models and agents Design monitoring, logging, tracing and incident-response patterns for live agents Apply governance, risk and compliance principles within client environments Support releases, environments and handover into client operations Ensure reliability, reproducibility, performance and cost control Building team capability Turn field learnings into reusable agent patterns, templates and accelerators Feed client insight back into the wider engineering team and its standards Coach peers and share new research, tools and techniques What you'll bring Essential 6 to 8 years in software engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong prompt engineering skills, including system prompt design, tool use and structured outputs Experience building evaluation frameworks for LLM or agent behaviour Strong Python, with experience building production-grade systems At least one major cloud AI ecosystem: Azure OpenAI, Google Vertex AI, AWS Bedrock or Anthropic Familiarity with semantic modelling, ontologies or knowledge graph concepts Proven ability to rapidly prototype solutions for client validation Experience working directly with clients in consulting or professional services Desirable Previous forward deployed, solutions engineering or technical leadership experience Commercial awareness and product thinking, including scoping and estimating technical work Agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel or the OpenAI and Anthropic agent SDKs LLM evaluation and observability tools such as Langfuse, LangSmith, Arize or Braintrust Deeper ontology or knowledge graph expertise Domain experience in life sciences, pharma or healthcare, FMCG or retail, insurance, or banking and financial services Strong engineers from non-AI backgrounds who have moved quickly into agentic AI and can show what they have built are very welcome.
Senior Forward Deployed Engineer LLM, RAG, Agentic AI and Prompt Engineering | Permanent | Remote first, UK A fast-growing, engineering-led AI consultancy is hiring Lead and Senior Forward Deployed Engineers on a remote-first basis across the UK. You will lead the design and delivery of complex client engagements spanning agentic systems, retrieval architectures and semantic layers, working directly inside enterprise client teams. Client demand for AI-led transformation is accelerating and our client is expanding its permanent engineering team across agentic systems, retrieval, ontologies and AI-enabled operations. As a Forward Deployed Engineer, you will work closely with the client, in their workshops and inside their systems, turning real operational problems into working AI solutions. These are senior, hands-on consulting roles that combine deep engineering craft with commercial and product thinking. You will work with senior client stakeholders alongside consulting and delivery colleagues, and lead technical design on your engagements. Prompt design, evaluation and guardrails are central to how you build, so the agents you ship are safe, reliable and measurable once they are live. You will shape both client outcomes and the firm's long-term AI engineering capability, while staying hands-on in the code. What you'll do Embedded client delivery and technical leadership Lead technical design within client engagements and set architectural direction across delivery teams Work alongside client teams, remotely and on occasional site visits, to understand their workflows, data and constraints first hand Turn ambiguous client problems into clear, feasible engineering approaches and fast prototypes for client validation Assess client data, platforms, security constraints and operating models Contribute to framing, use-case shaping and technical scoping discussions Work with client domain experts to surface edge cases and operational realities Produce clear, lightweight technical artefacts for client and internal audiences Act as the trusted technical voice on the engagement, from first workshop to go-live Agentic AI, LLM and RAG engineering Design and build LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops Build multi-agent architectures covering planning, delegation, tool use, memory, safety constraints and monitoring Build retrieval and vector-based systems: embeddings, RAG, structured reasoning and semantic workflows Connect agents to enterprise platforms through APIs, tools, MCP Servers and connectors Apply ontologies or knowledge graphs where they improve reasoning and data alignment Prompt engineering and evaluation Design, test and version system prompts, tool descriptions and agent instructions Build reusable prompt libraries and patterns that hold up across clients and use cases Create evaluation sets and automated eval loops to measure accuracy, safety and consistency Implement guardrails, structured outputs and explainability checks Diagnose and fix failure modes such as hallucination, tool misuse and prompt injection Delivery excellence, AI operations and reliability Implement evaluation frameworks and safety checks across models and agents Design monitoring, logging, tracing and incident-response patterns for live agents Apply governance, risk and compliance principles within client environments Support releases, environments and handover into client operations Ensure reliability, reproducibility, performance and cost control Building team capability Turn field learnings into reusable agent patterns, templates and accelerators Feed client insight back into the wider engineering team and its standards Coach peers and share new research, tools and techniques What you'll bring Essential 6 to 8 years in software engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong prompt engineering skills, including system prompt design, tool use and structured outputs Experience building evaluation frameworks for LLM or agent behaviour Strong Python, with experience building production-grade systems At least one major cloud AI ecosystem: Azure OpenAI, Google Vertex AI, AWS Bedrock or Anthropic Familiarity with semantic modelling, ontologies or knowledge graph concepts Proven ability to rapidly prototype solutions for client validation Experience working directly with clients in consulting or professional services Desirable Previous forward deployed, solutions engineering or technical leadership experience Commercial awareness and product thinking, including scoping and estimating technical work Agent frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel or the OpenAI and Anthropic agent SDKs LLM evaluation and observability tools such as Langfuse, LangSmith, Arize or Braintrust Deeper ontology or knowledge graph expertise Domain experience in life sciences, pharma or healthcare, FMCG or retail, insurance, or banking and financial services Strong engineers from non-AI backgrounds who have moved quickly into agentic AI and can show what they have built are very welcome.
Staffworx Limited
Senior Agentic AI Engineer. A fast-growing, engineering-led AI consultancy with industry recognised entrepreneur leadership is hiring Senior Agentic AI Engineers to design, build and run production AI systems for existing enterprise client base in retail, pharmaceutical, financial services and FMCG/Consumer Products. Permanent, remote first with some trips to office and clients. The opportunity Our client has more enterprise AI work than it can currently staff and is building out its permanent engineering team. You will be the hands-on engineer in a small, senior client delivery team, working alongside a consultant and a delivery lead. Together you take a loosely defined business problem through to a working, production-grade AI system that is safe, observable and maintainable. Evaluation, guardrails and monitoring are built in from day one. You will also help shape engineering standards and the reusable patterns the wider team builds on. This is a senior individual contributor role for someone who still loves building, not a step into management. What you'll do Agentic and AI systems engineering Design and build LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops Build multi-agent systems covering planning, delegation, tool use, safety constraints and monitoring Build retrieval pipelines: 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 through APIs, tools and connectors Set the technical direction on your engagements and shape the architecture of client solutions Discovery and solution design Turn ambiguous business needs into clear, practical engineering approaches Assess feasibility across data, systems and architecture Help shape use cases with consultants and explain clearly how a solution will work Work with client domain experts to understand edge cases and operational requirements Produce lightweight technical documentation, diagrams and decision records Quality, reliability and AI operations Implement evaluation frameworks and safety checks across model and agent behaviour Build monitoring, logging, tracing and incident-handling patterns Apply governance, risk and compliance principles to AI deployments Support release cycles, environments and operational readiness Own reliability, reproducibility and performance benchmarks Building team capability Turn what you build into reusable agent patterns, templates and starter kits Share new research, frameworks and techniques with colleagues Coach peers and influence coding standards and architectural practice What you'll bring Essential 6 to 8 years in software or application engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector stores Practical experience building multi-agent systems, agent orchestration or MCP-style tool patterns Strong Python, with a track record of production-grade workflows Experience with at least one cloud AI ecosystem: Azure OpenAI, Google Vertex AI, AWS Bedrock or Anthropic Working knowledge of semantic modelling, ontologies or knowledge graphs Ability to turn open-ended business problems into working prototypes quickly Comfortable in client-facing delivery, ideally within a consultancy Desirable Domain experience in life sciences, pharma or healthcare, FMCG or retail, insurance, or banking and financial services Deeper ontology or knowledge graph expertise Experience with LLM evaluation and observability tooling Strong engineers from non-AI backgrounds who have moved quickly into agentic AI and can show what they have built are very welcome Who thrives Senior and genuinely hands-on, with plenty of energy for building Self-starting and entrepreneurial, with a bias for action and ownership Calm and structured with ambiguity, reasoning from first principles A builder who prototypes, iterates and ships A clear communicator who can simplify complex systems for clients and colleagues Collaborative and low ego, at home in small senior teams Rigorous about safety, reliability and trust in AI systems Curious and quick to learn, keeping pace with new tools and research About Staffworx Staffworx is a UK-based specialist IT and consulting recruitment firm placing permanent, contract, interim and fractional talent across AI and machine learning, software engineering, data, cloud and senior consulting. We partner with software vendors, consultancies, fintechs and global systems integrators across the UK and EMEA, with growing activity in the US. For clients, we focus on quality over volume: a small number of well-screened candidates benchmarked against the brief, clear feedback loops and same-day responses. For candidates, we offer honest market insight, confidential conversations and introductions to roles that genuinely match your experience and ambitions. Whether you are scaling an AI engineering capability or exploring your next move in agentic AI, we would be glad to help.
Senior Agentic AI Engineer. A fast-growing, engineering-led AI consultancy with industry recognised entrepreneur leadership is hiring Senior Agentic AI Engineers to design, build and run production AI systems for existing enterprise client base in retail, pharmaceutical, financial services and FMCG/Consumer Products. Permanent, remote first with some trips to office and clients. The opportunity Our client has more enterprise AI work than it can currently staff and is building out its permanent engineering team. You will be the hands-on engineer in a small, senior client delivery team, working alongside a consultant and a delivery lead. Together you take a loosely defined business problem through to a working, production-grade AI system that is safe, observable and maintainable. Evaluation, guardrails and monitoring are built in from day one. You will also help shape engineering standards and the reusable patterns the wider team builds on. This is a senior individual contributor role for someone who still loves building, not a step into management. What you'll do Agentic and AI systems engineering Design and build LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops Build multi-agent systems covering planning, delegation, tool use, safety constraints and monitoring Build retrieval pipelines: 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 through APIs, tools and connectors Set the technical direction on your engagements and shape the architecture of client solutions Discovery and solution design Turn ambiguous business needs into clear, practical engineering approaches Assess feasibility across data, systems and architecture Help shape use cases with consultants and explain clearly how a solution will work Work with client domain experts to understand edge cases and operational requirements Produce lightweight technical documentation, diagrams and decision records Quality, reliability and AI operations Implement evaluation frameworks and safety checks across model and agent behaviour Build monitoring, logging, tracing and incident-handling patterns Apply governance, risk and compliance principles to AI deployments Support release cycles, environments and operational readiness Own reliability, reproducibility and performance benchmarks Building team capability Turn what you build into reusable agent patterns, templates and starter kits Share new research, frameworks and techniques with colleagues Coach peers and influence coding standards and architectural practice What you'll bring Essential 6 to 8 years in software or application engineering, AI engineering or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector stores Practical experience building multi-agent systems, agent orchestration or MCP-style tool patterns Strong Python, with a track record of production-grade workflows Experience with at least one cloud AI ecosystem: Azure OpenAI, Google Vertex AI, AWS Bedrock or Anthropic Working knowledge of semantic modelling, ontologies or knowledge graphs Ability to turn open-ended business problems into working prototypes quickly Comfortable in client-facing delivery, ideally within a consultancy Desirable Domain experience in life sciences, pharma or healthcare, FMCG or retail, insurance, or banking and financial services Deeper ontology or knowledge graph expertise Experience with LLM evaluation and observability tooling Strong engineers from non-AI backgrounds who have moved quickly into agentic AI and can show what they have built are very welcome Who thrives Senior and genuinely hands-on, with plenty of energy for building Self-starting and entrepreneurial, with a bias for action and ownership Calm and structured with ambiguity, reasoning from first principles A builder who prototypes, iterates and ships A clear communicator who can simplify complex systems for clients and colleagues Collaborative and low ego, at home in small senior teams Rigorous about safety, reliability and trust in AI systems Curious and quick to learn, keeping pace with new tools and research About Staffworx Staffworx is a UK-based specialist IT and consulting recruitment firm placing permanent, contract, interim and fractional talent across AI and machine learning, software engineering, data, cloud and senior consulting. We partner with software vendors, consultancies, fintechs and global systems integrators across the UK and EMEA, with growing activity in the US. For clients, we focus on quality over volume: a small number of well-screened candidates benchmarked against the brief, clear feedback loops and same-day responses. For candidates, we offer honest market insight, confidential conversations and introductions to roles that genuinely match your experience and ambitions. Whether you are scaling an AI engineering capability or exploring your next move in agentic AI, we would be glad to help.
