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For Companies At a Glance
- Tasks: Design and build cutting-edge AI systems, transforming business needs into reliable solutions.
- Company: Dynamic AI-native consulting firm with a focus on innovation and collaboration.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Join a high-agency environment and make a real impact on AI solutions from day one.
- Qualifications: 4-8 years in AI or software engineering with strong Python and LLM experience.
- Other info: Fast-paced, supportive culture with excellent career advancement opportunities.
The predicted salary is between 72000 - 100000 £ per year.
The AI Engineer is the technical core of an AI-native consulting and engineering team, focused on delivering enterprise-scale AI transformations. This role goes far beyond prompt engineering, owning the design, build, and operation of production-grade AI systems, including large language models (LLMs), multi-agent architectures, retrieval pipelines, and AI-enabled execution workflows. The engineer translates ambiguous business needs into safe, reliable, and measurable AI solutions while collaborating closely with consulting and orchestration teams.
Key Responsibilities:
- AI Engineering & Agentic System Design
- Design and implement production-grade LLM applications using modern orchestration patterns, prompt frameworks, and evaluation loops.
- Build and maintain multi-agent architectures, including planning, delegation, tool use, safety constraints, and monitoring.
- Develop retrieval and vector-based systems, embeddings, semantic workflows, and structured reasoning pipelines.
- Apply ontology and knowledge modeling literacy to support precise reasoning and data alignment.
- Integrate AI systems with APIs, tools, and enterprise connectors.
- Develop scalable prompt engineering solutions, including pattern libraries, guardrails, and explainability checks.
- Establish technical direction for projects and architectural foundations for client solutions.
- Technical Discovery & Solution Architecture
- Translate vague or ambiguous business requirements into clear engineering approaches.
- Conduct feasibility assessments across data, systems, and architecture.
- Contribute to use-case shaping and value-framing by providing technical clarity.
- Collaborate with domain experts to understand edge cases and operational requirements.
- Produce lightweight technical documentation, diagrams, and decision records.
- Deployment, Quality, AI Ops & Reliability
- Implement evaluation frameworks and safety checks for models and agent behaviour.
- Build monitoring pipelines, logs, traces, and incident-handling protocols.
- Apply governance, risk, and compliance principles in AI deployments.
- Support release cycles, environments, and operational readiness.
- Ensure reliability, reproducibility, and performance benchmarks are met.
- Internal Knowledge & Capability Building
- Develop reusable accelerators, agent patterns, and technical templates.
- Contribute to internal engineering curriculum and capability-building initiatives.
- Share emerging research, frameworks, and technical insights with the team.
- Influence coding standards, architectural patterns, and methodologies.
Required Experience & Skills:
- 4-8 years in AI engineering, ML engineering, software engineering, or applied data engineering.
- Strong hands-on experience with:
- LLMs (OpenAI, Anthropic, etc.)
- RAG pipelines
- Embeddings and vector stores
- Python proficiency at production level.
Very Strong Indicators:
- Multi-agent system design and orchestration (planning, delegation, tool integration).
- Evaluation frameworks, safety checks, and monitoring pipelines.
- AI Ops / observability experience.
- Familiarity with ontologies or semantic reasoning (literacy required).
Critical Distinction:
Candidates must be able to explain architectural trade-offs, discuss failure modes, demonstrate evaluation and reliability practices, and show ownership of systems in production. Shallow 'ChatGPT experience' alone will not meet the standard.
Why Join:
- High-agency environment with senior, collaborative teams.
- Ownership of client outcomes and direct impact on solution design.
- Deep engineering focus across agentic systems, semantic layers, and AI execution.
- Opportunity to shape the firm and its methodologies from day one.
- Fast-paced problem-solving with minimal bureaucracy.
- Early-stage company backed by significant investment with strong market traction.
Interview Flow:
- Initial technical discussion with CTO
- Second-stage interview with senior engineering leadership
- Final interview with founding team
AI Engineer employer: Develop
Contact Detail:
Develop Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Get your networking game on! Connect with professionals in the AI field through LinkedIn or industry events. We can’t stress enough how important it is to build relationships; you never know who might refer you to your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving LLMs and multi-agent systems. We want to see your hands-on experience, so make sure to highlight any production-level deployments you've worked on.
✨Tip Number 3
Prepare for those interviews like a pro! Brush up on your technical knowledge and be ready to discuss architectural trade-offs and evaluation practices. We love candidates who can demonstrate ownership of their systems and explain their thought process.
✨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, we’re always looking for passionate individuals who are eager to make an impact in the AI space.
We think you need these skills to ace AI Engineer
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, multi-agent systems, and any relevant projects you've worked on. We want to see how your skills align with our needs!
Showcase Your Technical Skills: Don’t just list your skills; demonstrate them! Include specific examples of how you've implemented AI solutions or tackled complex engineering challenges. This is your chance to shine and show us what you can bring to the table.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language and avoid jargon unless it's relevant. We appreciate a well-structured application that gets straight to the point while still showcasing your personality.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining our team at StudySmarter!
How to prepare for a job interview at Develop
✨Know Your AI Stuff
Make sure you brush up on your knowledge of large language models and multi-agent systems. Be ready to discuss your hands-on experience with LLMs and how you've deployed AI systems in the past. They’ll want to see that you can translate complex concepts into practical solutions.
✨Showcase Your Problem-Solving Skills
Prepare to tackle hypothetical scenarios during the interview. Think about how you would approach ambiguous business needs and turn them into clear engineering strategies. Practise explaining your thought process and the architectural trade-offs you’d consider.
✨Get Familiar with Their Tech Stack
Research the tools and technologies they use, especially around Python, retrieval pipelines, and embeddings. If you can demonstrate familiarity with their tech stack and how you’ve used similar tools in your previous roles, it’ll give you a leg up.
✨Be Ready for Client-Facing Questions
Since this role involves collaboration with consultants and orchestrators, be prepared to discuss your experience in client-facing situations. Share examples of how you’ve communicated technical concepts to non-technical stakeholders and how you’ve ensured successful project outcomes.
