AI Engineer

SLR Consulting; Greater London; On-site; GBP 70,000 - 100,000; Full time

Job summary

SLR Consulting seeks an AI Development Engineer to build production-grade AI systems at scale in a real-world environment. You will design and implement LLM-powered applications, orchestration, RAG, and tool integration, moving quickly from prototype to production.

Collaborating with product, engineering, and data teams, you will ship reliable services, deploy code in production, and iteratively improve systems based on user feedback.

Qualifications

• 2–5 years of software/AI engineering or ML systems experience.
• Experience building REST APIs and backend services.
• Proficiency in Python (preferred) or TypeScript.
• Strong understanding of system design and production troubleshooting.

Responsibilities

• Design production-grade AI systems powered by LLMs and modern frameworks.
• Develop applications using OpenAI, Anthropic, and other LLM APIs.
• Build MCP/tool integration layers and vector databases.
• Deploy and operate services in production with fast iteration.

Skills

Backend engineering

Python

TypeScript

REST APIs

System design

Debugging

Software lifecycle

Tools

Docker

AWS

GCP

Azure

Job description

SLR is seeking an AI Development Engineer who enjoys building AI systems that operate reliably in the real world. This role sits at the intersection of AI engineering, software development, and infrastructure, focusing on designing and implementing production-grade systems powered by large language models (LLMs).

You will work hands-on across the full delivery lifecycle—moving quickly from concept to prototype to production. Working closely with product, engineering, and data teams, you will help deliver intelligent applications built on modern AI infrastructure.

We value practical builders over academic theory. Success in this role is defined by your ability to design, implement, deploy, and operate real systems that deliver business value.

What You Will Build

You will design and implement systems across the AI stack, including:

• LLM-powered applications and intelligent agents
• Model orchestration and tool-use frameworks
• Retrieval systems and knowledge layers (RAG)
• MCP-style integration layers connecting models to tools, APIs, and data sources
• Scalable infrastructure supporting AI workloads

Your work will progress rapidly from prototype to production, with real users and real constraints.

Key Responsibilities

Build AI Systems

• Design and implement production-grade systems powered by LLMs and modern AI frameworks
• Develop applications using technologies such as:
• OpenAI, Anthropic and other LLM APIs
• LLM gateway
• Vector databases
• Agent orchestration frameworks

Implement AI Infrastructure

• Build and operate the infrastructure required to run reliable AI services, including:
• API services supporting AI applications
• Orchestration layers between models and tools

  Develop MCP and Tool Integration Layers

  • Design integration layers that enable models to interact with external systems, including:
  • API integrations
  • Tool-use systems for agents
  • Connectors to databases, SaaS tools, or internal platforms
  • Structured prompting and function-calling architectures

  Ship Production Code

  • Move quickly from concept to working product
  • Write clean, maintainable backend code
  • Build testable services
  • Deploy systems in production environments
  • Iterate based on real user feedback

  Collaborate Across Teams

  • Work closely with product managers, engineers, and designers to turn ideas into working solutions

  Required Skills

  Software Engineering Foundations

  • Strong backend engineering experience
  • Proficiency in Python (preferred) or TypeScript
  • Experience building REST APIs and backend services
  • Solid system design fundamentals
  • Debugging and production troubleshooting skills
  • Understand software development lifecycle

  LLM Application Development

  • Experience building applications using large language models
  • Prompt engineering and structured prompting
  • Tool use and function calling
  • Retrieval-Augmented Generation (RAG) architectures
  • LLM evaluation and iterative improvement

  Infrastructure and Deployment

  • Hands-on experience deploying production systems
  • Docker and containerization
  • Cloud platforms (AWS, GCP, or Azure)
  • CI/CD pipelines
  • Scalable service architecture

  Data and Retrieval Systems

  • Experience building and operating knowledge layers
  • Vector databases (e.g. Pinecone, Weaviate, pgvector)
  • Document ingestion pipelines
  • Embedding workflows
  • Search and retrieval optimization

  Nice To Have Experience With

  • MCP architectures or tool-connected AI systems
  • Agent frameworks
  • Knowledge graph systems
  • Streaming or event-driven systems
  • Distributed systems design
  • Evaluation frameworks for AI systems

  What we look for, we are looking for engineers who:

  • Prefer building working systems over discussing them
  • Move quickly while maintaining quality
  • Enjoy solving messy, real-world problems
  • Take ownership from prototype through to production
  • Stay curious about emerging AI capabilities

  You do not need to know everything—but you should be comfortable learning quickly and shipping continuously.

  Experience

  • 2–5 years of experience in software engineering, AI engineering, or ML systems

  We Value Evidence Of Building, Including

  • Shipped products
  • Real systems running in production
  • Open-source contributions
  • Side projects and experimentation

  Demonstrated delivery matters more than credentials.

  Why Join SLR

  Role

  You will help build real AI systems at a time when the AI stack is still rapidly evolving. This role offers:

  • Meaningful ownership and autonomy
  • Real engineering challenges
  • The opportunity to shape how intelligent software is designed, built, and deployed across SLR