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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.
- Benefits: Competitive salary, bonuses, personal learning budget, and flexible gear options.
- Why this job: Make a real impact in AI while collaborating with industry leaders.
- Qualifications: Experience with LLM APIs, strong Python skills, and cloud platform knowledge.
- Other info: Dynamic environment with huge career growth opportunities and a collaborative culture.
The predicted salary is between 36000 - 60000 ÂŁ per year.
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, optimizing 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 optimize 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 optimization strategies.
- Experience in specialized 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 Engineers London employer: Indicium Tech
Contact Detail:
Indicium Tech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineers London
✨Tip Number 1
Network like a pro! Connect with AI professionals on LinkedIn, attend meetups, and join relevant online communities. The more people you know in the industry, the better your chances of hearing about job openings before they’re even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and AI systems. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by brushing up on common AI engineering questions and practical coding challenges. Practice makes perfect, so consider mock interviews with friends or use platforms that offer interview prep.
✨Tip Number 4
Don’t forget to apply through our website! We love seeing applications directly from candidates who are excited about joining us at Indicium AI. It shows initiative and helps us get to know you better right from the start.
We think you need these skills to ace AI Engineers 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 vision!
Showcase Your Technical Skills: Don’t hold back on detailing your technical expertise! Include specific programming languages, frameworks, and tools you've worked with. We love seeing hands-on experience with Python and cloud platforms like AWS or GCP.
Be Clear and Concise: When writing your application, keep it clear and to the point. Use bullet points for easy reading and make sure to highlight your achievements. We appreciate straightforward communication that gets to the heart of your experience.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at Indicium Tech
✨Know Your LLMs
Make sure you brush up on your knowledge of large language models. Understand their capabilities, limitations, and failure modes. Be ready to discuss how you've implemented these technologies in past projects, as this will show your practical experience.
✨Showcase Your Coding Skills
Since strong Python proficiency is a must, be prepared to demonstrate your coding skills during the interview. You might be asked to solve a problem on the spot or discuss your approach to building production-grade applications. Bring examples of your clean code and error handling practices.
✨Discuss Real-World Applications
Be ready to talk about how you've tackled complex real-world problems using AI systems. Highlight any experience with multi-agent orchestration or intelligent tool integration. This will help the interviewers see how you can contribute to their projects right away.
✨Prepare for Technical Questions
Expect technical questions related to RAG architectures, vector databases, and evaluation strategies for LLM-based systems. Brush up on your knowledge of distributed systems and CI/CD processes, as these are crucial for the role. Being well-prepared will boost your confidence and impress the interviewers.