Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and build cutting-edge AI systems using large language models.
- Company: Join a fast-growing AI consultancy trusted by top enterprises worldwide.
- Benefits: Competitive salary, bonuses, personal learning budget, and flexible gear options.
- Other info: Dynamic environment with huge career growth opportunities and a collaborative culture.
- Why this job: Make a real impact in AI while collaborating with industry leaders.
- Qualifications: Experience with LLM APIs, Python, and cloud platforms required.
The predicted salary is between 60000 - 80000 £ per year.
About Indicium AI
Indicium AI is trusted by the world's leading enterprises to deliver AI into production at scale. We are a global AI-native consultancy with proven experience across Financial Services, Energy & Utilities, Healthcare & Life Sciences, Retail & CPG, and Manufacturing. From strategy, to build, to business outcomes, we unlock value from AI with unmatched clarity, speed, and capability. Powered by 600+ AI experts serving 50+ enterprise clients from 5 global locations, we work side-by-side with top partners - including Anthropic, Databricks, AWS, OpenAI, and Microsoft - to deliver modern AI with speed and measurable impact.
Overview
We’re seeking an experienced AI Engineer to design, build, and deploy production-grade AI systems powered by large language models. This role sits at the intersection of software engineering and AI implementation, focusing on building reliable, scalable applications rather than model training or research. You’ll work with cutting-edge LLM technologies, building advanced AI systems that solve complex real-world problems through multi-agent orchestration, intelligent tool integration, and robust production workflows. You’ll be crafting the orchestration layer that makes these systems production-ready—handling failure modes, optimising agent collaboration, and ensuring consistent, reliable outputs at scale. You’ll combine strong software engineering fundamentals with deep practical knowledge of LLM capabilities, limitations, and best practices for building non-deterministic systems that users can trust.
Responsibilities
- Design and implement production AI systems integrating LLMs, RAG pipelines, vector databases, and agentic frameworks.
- Create evaluation frameworks to measure and monitor system performance, accuracy, and reliability.
- Build and maintain production-grade AI applications with clean code, appropriate error handling, APIs, and data pipelines.
- Experience implementing, maintaining and evaluating retrieval systems (vector/graph databases, ingestion pipelines, chunking strategies, retrieval techniques such as HyDE).
- Implement feedback loops and observability to continuously improve system performance.
- Craft effective prompts and optimise for latency, cost, and quality across different model providers and configurations.
Required Skills and Experience
- Hands-on experience building applications with LLM APIs and deep understanding of their capabilities, limitations, and failure modes.
- Practical implementation of RAG architectures, vector databases, knowledge graphs and prompt engineering.
- Experience building multi-step LLM workflows and agentic systems using frameworks (e.g. SDK, Strands, Claude Agents SDK, LangGraph, etc.) or custom implementations where needed.
- Strong Python (or other modern programming language) proficiency with production API/service development experience and cloud platform knowledge (AWS, GCP, Azure).
- Understanding of distributed systems, CI/CD, testing frameworks, and deployment pipelines.
- Solid foundations and understanding of production-grade, cloud-native platform and infrastructure requirements, design, and implementation.
- Strong data manipulation skills (pandas, SQL) and understanding of evaluation strategies for LLM-based systems.
- Ability to work with ambiguity and optimise non-deterministic systems through a process of experimentation and evaluation while balancing latency/cost/quality tradeoffs.
Nice to Haves
- Experience with AI-assisted coding using tools like Claude Code, OpenAI Codex, Github Copilot.
- Experience with fine-tuning LLMs for domain-specific applications and knowledge of when fine-tuning is preferable to prompt engineering or RAG.
- Experience with real-time streaming, multimodal models, or search technologies like Elasticsearch.
- Familiarity with model observability tools (LangSmith, Weights & Biases) and cost optimisation strategies.
- Experience in specialised verticals (financial services, energy, healthcare, legal, retail) with understanding of compliance, security, and responsible AI practices.
- Experience with setting up tool calling agents, handoffs, and guardrails.
Why Indicium AI
- Fast-growing start-up organisation with huge opportunity for career growth.
- Highly competitive salary package along with company bonus.
- A hugely collaborative working environment where every person’s viewpoint is considered - a chance to make your mark on the business from day one!
- Financially backed business meaning security and support for new initiatives and global market expansion.
- Pick your own Gear! Macbooks, PCs, Accessories! Drive your development with a personal learning budget.
AI Engineer in City of London employer: Indicium AI
Contact Detail:
Indicium AI Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to current employees at Indicium AI on LinkedIn or other platforms. Ask them about their experiences and any tips they might have for landing the AI Engineer role. Personal connections can make a huge difference!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects related to LLMs, RAG architectures, or any relevant AI applications. This will give you an edge and demonstrate your hands-on experience when you get that interview.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python skills and understanding of distributed systems. Practice coding challenges and be ready to discuss your past projects in detail. We want to see how you think and solve problems!
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, it shows you’re genuinely interested in joining the team at Indicium AI.
We think you need these skills to ace AI Engineer in City of London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the AI Engineer role. Highlight your experience with LLMs, RAG architectures, and any relevant projects that showcase your skills. We want to see how you fit into our world!
Showcase Your Skills: Don’t just list your skills—demonstrate them! Include specific examples of how you've built production-grade AI systems or worked with multi-agent orchestration. This is your chance to shine, so let us know what you can do!
Be Clear and Concise: Keep your application clear and to the point. Use straightforward language and avoid jargon unless it’s relevant. We appreciate clarity, and it helps us understand your experience better!
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets to the right people. Plus, it shows us you’re serious about joining our team at Indicium AI!
How to prepare for a job interview at Indicium AI
✨Know Your LLMs Inside Out
Make sure you have a solid understanding of large language models and their capabilities. Be ready to discuss specific projects where you've implemented LLM APIs, and how you tackled their limitations. This will show your practical experience and depth of knowledge.
✨Showcase Your Coding Skills
Brush up on your Python or any modern programming language skills before the interview. Be prepared to demonstrate your ability to write clean, production-grade code, and discuss your experience with APIs and cloud platforms like AWS or GCP.
✨Prepare for System Design Questions
Expect questions about designing and implementing production AI systems. Think through how you would create evaluation frameworks, handle error management, and optimise workflows. Practising these scenarios can help you articulate your thought process clearly.
✨Emphasise Collaboration and Adaptability
Indicium AI values a collaborative environment, so be ready to share examples of how you've worked in teams. Discuss how you've navigated ambiguity and adapted your approach based on feedback or changing requirements, showcasing your problem-solving skills.