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- Tasks: Develop innovative AI software and shape the future of deal-making technology.
- Company: Exciting AI start-up with a mission to revolutionise the deal process.
- Benefits: Competitive salary, equity options, and fully remote work.
- Why this job: Join a dynamic team and make a real impact in AI development.
- Qualifications: 5+ years in full-stack development with strong AI and database skills.
- Other info: Great opportunity for career growth in a fast-paced environment.
Full Stack AI Software Engineer – Full Remote UK – £90,000 + Equity
Find out exactly what skills, experience, and qualifications you will need to succeed in this role before applying below.
This role requires a software engineer with experience in implementing RAG pipelines and Vector Search (and hybrid AI searches, preferably).
The client I am working with is an AI focused start-up backed by a £1.7M pre-seed investment. They are on a mission to streamline the deal making process, with the founders having had firsthand experience of the lengthiness and challenges of deal processes in the past. Their stack spans across back end, front end, data and crucially AI.
This is a great opportunity for an entrepreneurial software engineer who wants to play a part in shaping the technical vision of this business and work on their product from an early stage.
What you\’ll work on:
Backend APIs (Python/FastAPI): Build and maintain secure, high-performance services that drive AI features and data access at scale.
RAG & vector search: Design and improve retrieval pipelines (embeddings, chunking, hybrid search, ranking, feedback loops), owning schema design, latency, and relevance across vector databases.
LLM integration: Connect and orchestrate large language models (OpenAI, Bedrock, etc.), manage prompts, tools, safeguards, and evaluation.
Data pipelines: Ingest, clean, and transform structured and unstructured data; design efficient schemas (Postgres/NoSQL) for search and analytics.
Frontend (React/Next.js): Deliver user-friendly, performant UIs that make AI-powered features (search, filters, explanations, citations) clear and accessible.
Architecture: Shape a modular, scalable platform on AWS (ECS), separating ingestion, retrieval, reasoning, and delivery.
Quality & reliability: Ensure reliability through testing, CI/CD, observability (metrics/tracing for LLM and retrieval paths), and performance optimisation.
Collaboration: Partner with product and leadership teams, mentor peers, and play a role in shaping technical direction.
Innovation: Explore and recommend new technologies, frameworks, and methods for both full-stack and AI development.
Requirements:
A motivated, hands-on engineer with an ownership mindset, strong communication skills, and a collaborative approach.
5+ years’ experience in full-stack development.
Strong background in RAG systems, vector databases (pgvector, FAISS, Weaviate, Elasticsearch k-NN), embeddings, and hybrid search methods.
Practical knowledge of chunking strategies, indexing, precision/recall trade-offs, reranking, and evaluation techniques.
Proficient in Python (FastAPI) and React/Next.js.
Solid experience with both SQL and NoSQL databases (Postgres, DynamoDB, etc.).
Exposure to LLMs, embeddings, and vector search APIs.
Strong understanding of data engineering, schema design, ETL, and optimisation.
Proficiency with cloud (AWS preferred) and containerised deployments (Docker, ECS).
Knowledge of secure coding practices and managing sensitive data.
Excellent communication, problem-solving, and leadership skills.
Nice to have
Experience with rerankers (e.g., cross-encoders), hybrid retrieval (SQL + vectors), query expansion, or lightweight knowledge graphs.
Familiarity with LLM evaluation tooling (LangChain, LlamaIndex, OpenAI Evals) and observability for cost, relevance, and latency.
Background in B2B data products or fintech
Full Stack AI Software Engineer employer: Ada Meher
Contact Detail:
Ada Meher Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Full Stack AI Software Engineer
✨Tip Number 1
Network like a pro! Reach out to people in the AI and software engineering space, especially those who work at companies you're interested in. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repository showcasing your projects, especially those related to RAG pipelines and vector search. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice explaining complex concepts simply, as communication is key in collaborative environments like startups.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us. Tailor your application to highlight how your experience aligns with our mission and tech stack.
We think you need these skills to ace Full Stack AI Software Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experience mentioned in the job description. Highlight your work with RAG pipelines, vector databases, and any relevant AI projects. We want to see how you fit into our mission!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Share your passion for AI and software engineering, and explain why you're excited about this role. Let us know how your entrepreneurial spirit aligns with our start-up vibe.
Showcase Your Projects: If you've worked on relevant projects, whether personal or professional, make sure to include them. We love seeing practical examples of your skills, especially in full-stack development and AI integration.
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 don’t miss out on any important updates from our team!
How to prepare for a job interview at Ada Meher
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, especially Python, FastAPI, and React/Next.js. Brush up on your knowledge of RAG systems and vector databases like pgvector and Elasticsearch, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous projects, particularly those related to AI and data pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight how you tackled complex problems effectively.
✨Demonstrate Your Ownership Mindset
This role requires a hands-on engineer with an ownership mindset. Be ready to share examples of how you've taken initiative in past roles, whether it was leading a project, mentoring peers, or suggesting new technologies that improved processes.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s vision, the team dynamics, and the technical challenges they face. This shows your genuine interest in the role and helps you assess if the company aligns with your career goals.
