Forward Deployed AI Engineer

Forward Deployed AI Engineer

Contract: 3–6 months

Location: London – Hybrid

Engagement: Contract

Industry: AI / Technology Consultancy

The Opportunity

We are working with a fast-growing technology consultancy that is looking for a Forward Deployed AI Engineer to join them on an initial 3–6 month contract.

You will work directly with the consultancy’s end clients, predominantly SMEs and high-growth scale-ups across a range of sectors, helping them identify, build and deploy practical AI solutions into their businesses.

This is a highly client-facing, hands-on engineering role. You will sit at the intersection of AI engineering, product, consulting and customer delivery, taking problems from an initial business requirement through to a working production solution.

Rather than working on one product internally, you will move across different client environments and use cases, rapidly understanding their businesses and deploying AI solutions that deliver tangible value.

What You’ll Be Doing

• Work directly with founders, CTOs, product teams and business stakeholders to understand their problems and identify where AI can create measurable value.
• Design, prototype and deploy production-ready Generative AI and LLM-based applications.
• Build AI-powered workflows, agents, copilots, search/RAG applications and automation solutions.
• Take solutions from initial proof-of-concept through to production deployment.
• Integrate AI applications into existing client platforms, APIs, data sources and business systems.
• Work with structured and unstructured data to create reliable AI applications.
• Build and optimise RAG pipelines, embeddings, vector search and retrieval systems.
• Develop agentic workflows using modern LLM orchestration frameworks.
• Evaluate models and architectures based on performance, latency, reliability and cost.
• Work closely with client engineering teams to ensure solutions are scalable, secure and maintainable.
• Translate complex technical concepts into clear recommendations for non-technical stakeholders.
• Help clients move quickly from AI experimentation to real-world adoption.

Technical Experience

We’re looking for someone with strong experience across modern AI engineering, ideally including:

• Strong Python engineering experience.
• Hands-on experience building applications using LLMs and Generative AI.
• Experience with models and APIs from providers such as OpenAI, Anthropic, Google or open-source alternatives.
• Experience building RAG architectures, embeddings and vector databases.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex or similar.
• Experience building AI agents, tool-calling workflows and multi-step AI applications.
• Strong API development and systems integration experience.
• Experience deploying applications into cloud environments such as AWS, Azure or GCP.
• Understanding of production AI considerations including evaluation, observability, security, latency and cost optimisation.
• Experience working with databases, APIs and modern backend architectures.

Experience with some of the following would also be beneficial:

• Claude / Claude Code
• MCP
• FastAPI
• Docker / Kubernetes
• Terraform
• Serverless architectures
• Vector databases such as Pinecone, Weaviate, Qdrant or pgvector
• AI evaluation and observability platforms
• Fine-tuning or model optimisation
• Data engineering / data pipelines

What We’re Looking For

The strongest candidates will be engineers who are comfortable operating in ambiguous environments and enjoy working directly with customers.

You should be:

• Highly hands-on and comfortable building solutions yourself.
• Able to rapidly understand a new business, technical environment and use case.
• Comfortable moving between multiple client projects and industries.
• Strong in front of customers and senior stakeholders.
• Pragmatic rather than purely research-focused — focused on getting useful AI applications into production.
• Comfortable owning delivery from discovery and architecture through to implementation.
• Able to balance engineering quality with the pace required by SMEs and scale-ups.

Previous experience within a consultancy, startup, scale-up, solutions engineering or Forward Deployed Engineering environment would be particularly relevant.