Unique AI
Sr.Backend/ Tech Lead, RAG & Knowledge Ingestion

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Job Description
Defining how AI and finance work together — securely, intelligently, and at scale.
We at Unique AI are building a highly secure generative AI platform, tailored for the financial sector. Utilizing AI, LLM, agentic and Skills technologies, we aim to revolutionize the Financial industry with an AI Automated workforce. Our mission is to increase efficiency, improve alpha, reduce risk, and increase quality across everything we do. We offer an inclusive environment at Unique and we encourage all genders and all neuro-diversities to apply.
We're building an enterprise AI platform where knowledge ingestion is a foundational capability. It is how customer documents, files, and unstructured data enter the system so that AI assistants can search, retrieve, and reason over it.
We're looking for a backend-focused tech lead to own the technical direction of this platform end to end. This is a hands-on technical leadership role. You will set architecture, guide implementation across the core ingestion services, mentor engineers through design and code review, and drive improvements in reliability, throughput, retrieval quality, and maintainability.
Job Requirements
- 7+ years of backend engineering experience with strong expertise in TypeScript and Node.js
- Experience as a technical lead for a backend or platform domain, with ownership of architecture, technical direction, and mentoring
- Strong experience building ingestion or unstructured data pipelines, ideally involving document parsing across formats such as PDF, Office documents, HTML, or email
- Strong database, indexing, and retrieval experience across PostgreSQL, Elasticsearch, Qdrant, or similar systems
- Experience improving retrieval quality in production using embeddings, vector search, or RAG-style retrieval systems
- System design maturity: you can design for scale, failure handling, service boundaries, and pragmatic trade-offs under real constraints
- Multi-tenant SaaS experience: you understand data isolation, tenant-scoped processing, and enterprise security requirements
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
Job Responsibilities
- Technical leadership: Define and own the RAG platform’s architecture and standards, make trade-offed design decisions, drive high-impact backend improvements across the pipeline, and communicate a clear technical roadmap.
- Hands-on backend engineering: Build and evolve ingestion, processing, and retrieval services/APIs, strengthen async pipelines (RabbitMQ, retries, backpressure), enhance parsers, and optimize indexing on Qdrant/Elasticsearch for quality and latency.
- Retrieval and document intelligence: Partner with AI engineers to advance chunking, embeddings, metadata, and RAG patterns; integrate Python services for agentic workflows; improve retrieval via better indexing/enrichment/evaluation and adopt OCR/layout/table extraction when it moves the needle.
- Technical leadership for the team: Mentor and unblock engineers through reviews and pairing, elevate reliability/performance/observability/testing, and support hiring and onboarding without primary people-management duties.
- Platform ownership: Run production delivery via Helm/Kubernetes and ArgoCD GitOps, enhance observability with OpenTelemetry/Prometheus/logging/alerts, and enforce multi-tenant data isolation, security, and auditability end to end.


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Job Benefits
- Leading Company 💪: A chance to be part of a leading company in a rapidly evolving industry
- Innovative & Impactful Projects 🚀: Engage in groundbreaking projects within the Generative AI space. Contribute towards building the foundations enabling financial institutions to operationalize AI.
- Ownership & Autonomy 🧠: We care about outcomes and the right solution crafted with clean code
- Continuous Learning 🔄: AI is moving fast and we value engineers who keep learning and experimenting
- Culture 🌿: A modern company with flat hierarchies that focuses on a people-oriented culture and places great emphasis on transparency and open communication
- Hybrid set up 🧑💻🏢: Based in London with flexibility for remote days
- Security-first culture 🤝: Develop systems trusted by leading financial institutions.
- Modern stack ⚙️: NestJS, TypeScript, Prisma, PostgreSQL, Kubernetes, Azure.
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