Build and deploy production AI solutions, agents and automations, partnering with business teams to solve high-value problems and drive AI adoption.
12th August, 2026
Enterprise AI Project
Build & Deploy Production AI Solutions
6 months - with the view to extend
We are seeking a Forward Deployed AI Engineer to work directly with business teams to identify high-value problems and build, deploy and iterate AI-powered solutions that deliver measurable business outcomes. This is a hands-on role spanning AI engineering, software development, LLMs, agents and enterprise integrations. You will work with stakeholders to understand their workflows, prototype solutions and take them through to production.
Key Responsibilities:
Partner with business teams to identify and scope high-value AI opportunities.
Design, build and deploy production-grade AI applications, agents and automations.
Develop solutions using LLMs, RAG, prompt engineering and agentic AI.
Integrate AI solutions with enterprise data, APIs and systems.
Own solutions from discovery and prototyping through to production and ongoing improvement.
Build reusable AI skills, tools and components.
Drive adoption through documentation, onboarding and stakeholder enablement.
Apply security, privacy and responsible AI principles to all solutions.
Share learnings and reusable components across the wider AI engineering team.
Experience/ Skills Required:
Strong professional experience building and shipping production software.
Hands-on experience applying AI/ML, LLMs or Generative AI to real-world problems.
Strong Python skills, plus JavaScript/TypeScript or a comparable language.
Strong SQL and experience working with APIs and enterprise systems.
Experience with LLM frameworks such as LangChain, LangGraph or Microsoft Agent Framework.
Experience with vector databases or search technologies such as Pinecone, Weaviate, pgvector or Azure AI Search.
Experience with Azure, AWS or GCP and services such as Azure OpenAI, Bedrock or Vertex AI.
Understanding of RAG, agents, prompt engineering, evaluation and LLM observability.
Experience with Git, CI/CD and modern deployment practices.
Strong communication skills and the ability to work directly with non-technical stakeholders.
Desirable Experience in solutions engineering, consulting, internal AI platforms, forward-deployed engineering, agentic AI, cloud/data platforms or enterprise governance will be highly regarded. This is an opportunity to combine engineering with real-world AI delivery - building solutions that are used by the business, not just prototypes.