Canary Wharf (4 days/week on-site)
We are recruiting on behalf of an exciting end client who is building high-impact AI-powered applications that deliver real business value at speed — and they need engineers who think holistically, automate relentlessly, and are fluent in the fast-moving world of AI tooling and infrastructure.
This isn't a research role. It's for seasoned engineers who are excited about applying AI in practical, scalable, and production-grade ways.
What You'll Be Doing
- Designing, developing, and maintaining production-grade AI applications using modern engineering practices (CI/CD, testing, observability, cloud-native)
- Building foundational platforms — conversational bots, AI-powered search, unstructured data processing, GenBI — that scale across the enterprise
- Working in cross‑functional, embedded teams alongside business stakeholders to ship AI solutions fast
- Evaluating and integrating LLMs, vector DBs, RAG patterns, and AI agents into robust applications
- Championing automation across data ingestion, evaluation, model integration, and deployment
- Contributing to internal tooling libraries and AI engineering best practices
What Our Client Is Looking For
- 5+ years of professional software engineering experience, with a track record of shipping complex systems in production
- Strong Python skills (preferred), or Java/similar; solid experience with microservices, APIs, and backend systems
- Experience integrating ML models into production (LLMs via APIs, RAG, fine-tuning, embeddings, agents)
- Solid grasp of software architecture, cloud infrastructure (AWS, Azure, or GCP), and modern DevOps
- Ability to move fast without sacrificing code quality or operational excellence
- Hands‑on experience with LangChain, Haystack, Hugging Face, Weaviate, or similar AI/ML tooling
- Experience with agentic frameworks: LlamaIndex, CrewAI, AutoGen, or similar LLM orchestration toolkits
- Background in platform engineering or internal developer tools
- Experience with unstructured data (documents, conversations, images) and transforming it into structured formats
- Prior work in a forward‑deployed or embedded team model
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