Strong grounding in probabilistic models , causal inference , and AI reasoning approaches. Hands-on experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Solid understanding of vector databases (e.g. Pinecone, Weaviate, Milvus) and retrieval-augmented generation (RAG) techniques. Comfort with rapid technical recall for industry benchmarks, model parameters, and framework capabilities. More ❯
Experience 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 More ❯
models (BERT, GPT) and prompt engineering for sentiment tasks Background building active learning and annotation pipelines to bootstrap training data Familiarity with semantic search or vector databases (Elasticsearch, FAISS, Pinecone) for topic modeling and similarity queries Familiarity with crypto markets, order books, and risk-management frameworks Familiarity with anomaly detection methods for streaming text and time series data Experience developing More ❯
and reliability Essential Skills Strong Python skills and experience with Hugging Face Transformers Familiarity with LLM fine-tuning and inference optimisation Experience with vector search and embeddings (e.g. FAISS, Pinecone) Understanding of prompt engineering and few-shot learning Ability to work independently in a hybrid, agile environment Nice to Have Experience with LangChain, LlamaIndex, or similar orchestration tools Exposure to More ❯
rapid, iterative cycles with weekly releases Prompt Engineering: Able to craft, test, and refine prompts for outputs like chat, image, or video LLM + Vector Tools: Experience with embeddings, Pinecone/Weaviate, and building RAG-style pipelines Why Join Magic Group? No red tape. Just build, launch, and scale A small, senior team with high trust and even higher impact More ❯
Qualifications Strong background in Computer Science and Software Development Experience with complex RAG pipelines (without using Langchain or LlamaIndex) Tech stack includes: Foundation and open-source LLMs, vector databases (Pinecone, Qdrant, Weaviate), embeddings, Next.js, Vercel, MongoDB, Cohere reranking, multimodal parsing (e.g., Unstructured.io) This is a project-based, part-time, contract role. Must provide work samples (GitHub). Must be based More ❯
new tech quickly Experience mentoring junior engineers Experience interacting with multiple stakeholders Enjoyable to work with TECHNOLOGY STACK Python, PostgreSQL, FastAPI, Redis, TypeScript, React, Next.js, Tailwind, AWS, Kubernetes, Prometheus, Pinecone, GPT-4 EXAMPLE PROJECTS Craft plan to measure and improve our search engine Improve and migrate our data model for the content we host Migrate our NLP algorithms over to More ❯
prompt tweaks changed an LLM's output to match a specific product need. Nice-to-Haves LangChain, LlamaIndex, or any RAG experiment on your GitHub. Vector database dabbling (pgvector, Pinecone). A side project people outside your family have used. Our Stack React 18 Next.js Node 20 FastAPI OpenAI & Anthropic APIs Postgres + pgvector Vercel Fly.io GitHub Actions Cursor & Windsurf More ❯
Strong sense of UX Good systems design Ability to learn new tech quickly Enjoyable to work with TECHNOLOGY STACK Python, PostgreSQL, FastAPI, Redis, TypeScript, React, Next.js, Tailwind, AWS, Kubernetes, Pinecone, GPT-4 EXAMPLE PROJECTS Use an LLM to identify references to other sections in the text of the law Create Rap Genius-style annotations on sections of building code, to More ❯
solving complex problems Good systems design Ability to learn new tech quickly Enjoyable to work with TECHNOLOGY STACK Python, PostgreSQL, FastAPI, Redis, TypeScript, React, Next.js, Tailwind, AWS, Kubernetes, Prometheus, Pinecone, GPT-4 EXAMPLE PROJECTS Use an LLM to identify references to other sections in the text of the law Improve and migrate our data model for the content we host More ❯
controllers. Develop and maintain AI microservices using Docker, Kubernetes, and FastAPI, ensuring smooth model serving and error handling; Vector Search & Retrieval: Implement retrieval-augmented workflows: ingest documents, index embeddings (Pinecone, FAISS, Weaviate), and build similarity search features. Rapid Prototyping: Create interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/… experience fine-tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven rapid-prototyping skills using Streamlit or equivalent frameworks for UI demos. Familiarity with containerization (Docker) and at least one orchestration/deployment platform More ❯
Experience working with LLMs and prompt design (ideally OpenAI or similar) An understanding of structured data (SQL, JSON, CSV) and unstructured text (PDFs Exposure to RAG architectures and vector stores (such as FAISS, Pinecone...) Prompt Engineer (AI & Data) – Could suit More ❯
