AI Engineer (Expression of Interest)

Are you a proactive, team-oriented, and motivated AI Engineer looking for your next challenge? We’re always on the lookout for passionate individuals who share our values and are excited to make an impact.

We value a go-getter attitude, a drive to get things done, and the empathy to truly understand our clients’ needs. Our team takes pride in working hard-and having fun while doing it. We believe in the power of collaboration and bringing like-minded people together to build great things. We thrive on tackling the most challenging questions of today, making our daily work engaging and our team cohesive. We’re seeking an AI Engineer with expertise in agentic AI systems, LLM applications, and modern AI infrastructure to join our dynamic team and help build innovative AI-powered products across various industries.

Please note that this is an open call for expressions of interest rather than an active vacancy. We are proactively building our AI pipeline and would love to review your background for future roles. If your profile aligns with our vision, we will be in touch when the right project kicks off.

What is it all about?

• Design, build, and deploy production agent systems and LLM-powered applications for enterprise clients in regulated industries.
• Architect RAG pipelines end-to-end: chunking strategies, embedding selection, vector database design, retrieval optimization, hybrid search.
• Implement agent systems with tool use / function calling, multi-step workflows, human-in-the-loop patterns, and state management.
• Design prompt architecture at a systems level: system prompts, prompt composition, structured output, prompt versioning as deployable artifacts.
• Build evaluation harnesses: golden datasets, LLM-as-judge pipelines, regression testing, quality monitoring for non-deterministic systems.
• Instrument applications for production observability: tracing, cost attribution, quality drift detection.
• Manage LLM cost optimisation: prompt caching, model routing, batch processing, and token budgets.
• Deploy and operate self-hosted LLMs on our on-premise GPU infrastructure (NVIDIA H200 cluster): model serving with vLLM, inference optimisation, GPU utilisation tuning, and choosing when to route to local vs. hosted models based on cost, latency, and data sensitivity.
• Integrate AI systems with client enterprise infrastructure – APIs, databases, identity systems, compliance layers.
• Mentor backend developers transitioning into AI engineering.
• Collaborate with Solution Architects on system design, AI Ops engineers on production handoff, and Use-Case Consultants on technical qualification of new opportunities.
• Contribute to AI product discovery, rapid prototyping, and innovation initiatives across internal and client-facing projects.
• Support pre-sales and solution design activities by translating business challenges into practical AI architectures and delivery approaches.
• Stay at the forefront of the modern AI ecosystem and help drive adoption of emerging patterns, frameworks, and best practices across the organisation.

What do we expect?

• 5+ years of backend engineering experience. Python required; TypeScript, Java, or C# is a plus.
• Strong fundamentals in APIs, databases, authentication/authorization, and scalable backend application design.
• Hands-on production experience building LLM-powered applications used by real users – beyond prototypes, demos, or experimental side projects.
• Production RAG implementation experience: embedding pipelines, vector databases (pgvector, Pinecone, Weaviate, or equivalent), and retrieval quality evaluation.
• Experience building agentic workflows with tool use, structured outputs, and multi-step orchestration.
• Experience building evaluation suites for LLM outputs (golden datasets, LLM-as-judge, regression testing).
• Familiarity with security, privacy, and compliance considerations when deploying AI systems in enterprise environments.
• Ability to evaluate emerging AI frameworks, tooling, and model capabilities pragmatically in fast-evolving ecosystems.
• Understanding of LLM cost structures and hands-on optimisation experience (caching, routing, token budgets)
• Experience integrating LLM systems with enterprise infrastructure.
• Engineering discipline: Git, Docker, CI/CD, code review, documentation.
• Experience with agent frameworks (LangGraph, Anthropic agent patterns, OpenAI Agents SDK) is a strong plus.
• Experience with self-hosted/open-source LLM deployment and inference infrastructure is a strong plus.
• Familiarity with LLM observability tools (Langfuse, LangSmith, Arize) is a strong plus.
• Familiarity with MCP (Model Context Protocol) is a plus.
• Cloud AI services experience (AWS Bedrock, Azure OpenAI, GCP Vertex AI) is a plus.
• Experience in healthcare, media, or government/public sector verticals is a plus.
• Cloud AI certification (Azure AI-102, AWS AI Practitioner) is a plus.
• Excellent communication skills, with the ability to convey technical decisions to both technical and non-technical stakeholders.
• Advanced English language skills.

What we offer:

• The location choice is yours: remote, on-site or hybrid
• Flexible working hours
• Work with new technologies in a high-performance environment
• Access to cutting-edge AI infrastructure, including enterprise-grade on-premise GPU (H200) clusters and modern LLM tooling.
• Direct input on architecture decisions for client projects and internal R&D initiatives.
• Diverse international projects
• IT community involvement — Meetups, Workshops & Articles
• Internal workshops, personal development and certifications
• 100% paid sick leave
• Paid health insurance
• Subvention of Multisport card
• Transport allowance & meal allowance

Salary range:

Our salaries are based on your experience, level of knowledge & technical interview.

We will contact only the candidates who might be the best fit for future opportunities.

Sounds exciting? Click on the button below and apply now 🙂