building with generative AI applications in production environments. Expertise with microservices architecture and RESTful APIs. Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar. Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes). You are committed to writing clean, maintainable, and scalable code, following best practices in software More ❯
building with generative AI applications in production environments. Expertise with microservices architecture and RESTful APIs. Solid understanding of database technologies such as PostgreSQL and vector databases as Elastic, Pinecone, Weaviate, or similar. Familiarity with cloud platforms (AWS, GCP, etc.) and containerized environments (Docker, Kubernetes). You are committed to writing clean, maintainable, and scalable code, following best practices in software More ❯
tools Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3–5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs More ❯
tools Cloud & MLOps (AWS): Deploy with SageMaker, Bedrock, Lambda, S3, ECS, EKS Full-Stack Integration: Build APIs (FastAPI, Flask) and integrate with React, TypeScript, Node.js Vector Search: Use FAISS, Weaviate, Pinecone, ChromaDB, OpenSearch Required skills & experience: 3–5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs More ❯
Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding of More ❯
Python, with expertise in using frameworks like Hugging Face Transformers, LangChain, OpenAI APIs, or other LLM orchestration tools. A solid understanding of tokenization, embedding models, vector databases (e.g., Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) pipelines. Experience designing and evaluating LLM-powered systems such as chatbots, summarization tools, content generation workflows, or intelligent data extraction pipelines. Deep understanding of More ❯
and building innovative AI-driven products Comfortable working in an agile, fast-paced startup environment DESIRABLE SKILLS Experience with media processing tools (FFmpeg, WebRTC) Familiarity with vector databases (Pinecone, Weaviate) and embedding workflows Background in generative audio/video models or multimodal AI systems HOW TO APPLY Please register your interest by sending your CV and portfolio via the apply More ❯
experience building AI systems with LLMs — you’ve worked with tools like LangChain, LlamaIndex, Haystack, or built your own stack Hands-on with embedding models, vector DBs (e.g., FAISS, Weaviate, Qdrant), and retrieval logic Strong Python engineering skills — you write clean, production-ready code with tests Experience building and evaluating RAG pipelines in a real-world setting Familiarity with LLM More ❯
experience building AI systems with LLMs — you’ve worked with tools like LangChain, LlamaIndex, Haystack, or built your own stack Hands-on with embedding models, vector DBs (e.g., FAISS, Weaviate, Qdrant), and retrieval logic Strong Python engineering skills — you write clean, production-ready code with tests Experience building and evaluating RAG pipelines in a real-world setting Familiarity with LLM More ❯
architectures such as GPT, BERT, or T5. Experience training and fine-tuning smaller deep learning models for NLP and computer vision tasks. Knowledge of vector databases (e.g., FAISS, Pinecone, Weaviate) and their use in AI-driven retrieval systems. Familiarity with anomaly detection techniques using deep learning and traditional ML approaches. Experience working with large-scale data processing frameworks (e.g., Spark More ❯
more specifically with: GCP BigQuery (we use it to power our analytics) OCR engines (we use AWS Textract, GDocAI, and we have used tesseractOCR in the past) Prompt Engineering Weaviate (we use it for RAG in LLM powered tasks and for hybrid searches) Kubernetes (we run Weaviate and other specific services on Kubernetes) CircleCI DataDog Auth0 (we use it, but More ❯
You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery, Pub/ More ❯
You’ll Do Build scalable backend microservices in Python (FastAPI) to support RAG workflows and user queries Develop and optimise vector search pipelines using tools like PGVector, Pinecone, or Weaviate Design embedding orchestration and hybrid retrieval mechanisms Implement evaluation frameworks (BLEU, ROUGE, hallucination checks) to monitor answer quality Deploy production systems on GCP (Cloud Run, Vertex AI, BigQuery, Pub/ More ❯
What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices Collaborate More ❯
in our proprietary job function and task data. Build data pipelines to ingest, process, and embed the data required for the RAG system, likely using vector databases (e.g., Pinecone, Weaviate, Chroma). Integrate and manage calls to image generation models to create the visual avatars for the synthetic personas. Establish frameworks and metrics to rigorously evaluate the quality, accuracy, and More ❯
What You’ll Own Architect and develop backend microservices (Python/FastAPI) that power our RAG pipelines and analytics Build scalable infrastructure for retrieval and vector search (PGVector, Pinecone, Weaviate) Design evaluation frameworks to improve search accuracy and reduce hallucinations Deploy and manage services on GCP (Vertex AI, Cloud Run, BigQuery) using Terraform and CI/CD best practices Collaborate More ❯
to justify your pick. • Experience extending an *AWS stack (Terraform, ECS Fargate, ALB, Secrets Manager, KMS)*. • Hands-on with *LLM APIs* and at least one *vector database* (Pinecone, Weaviate, OpenSearch, etc.). • Multi-tenant data design with GDPR awareness. • CI/CD and automated-testing mindset. Nice-to-Haves • *Solution-architect background*—ability to map future services and scaling More ❯
Strong coding abilities in Python (Sanic); React (MUI) and other relevant frameworks Experience with secure cloud infrastructure (AWS, GCP, Azure) for enterprise applications • Knowledge of vector databases (FAISS, Pinecone, Weaviate) and optimization techniques • Direct experience building technology solutions for enterprise clients • Understanding of enterprise data processing and documentation requirements • Knowledge of compliance and security considerations in enterprise technology • Experience with More ❯
Our benefits Share Options (EMI) scheme 25 days annual leave, plus flexible bank holidaysand the opportunity to buy additional days Enhanced workplace Pension scheme - opt in salary sacrifice scheme Life Insurance (3x annual salary) Employee Assistance Programme (EAP) and workplace More ❯
Our benefits Share Options (EMI) scheme 25 days annual leave, plus flexible bank holidaysand the opportunity to buy additional days Enhanced workplace Pension scheme - opt in salary sacrifice scheme Life Insurance (3x annual salary) Employee Assistance Programme (EAP) and workplace More ❯
