Design and build AI-powered features, pipelines, and automation workflows from scratch
Integrate and fine-tune LLMs, embedding models, and other ML systems into production applications
Develop and maintain RAG pipelines, vector search systems, and agent-based architectures
Write clean, well-structured code across backend and API layers to support AI feature delivery
Evaluate, benchmark, and iterate on model outputs to ensure quality and reliability
Collaborate with cross-functional teams to scope requirements and architect AI solutions
Stay current with the rapidly evolving AI landscape and proactively introduce relevant tooling and approaches
Document technical designs, system behaviour, and deployment processes clearly and thoroughly

Strong programming skills in Python
Hands-on experience building with LLMs (OpenAI, Anthropic, Mistral, or similar) via API and SDK
Practical experience with RAG architectures, vector databases (Pinecone, Weaviate, Chroma, etc.), and prompt engineering
Familiarity with AI agent frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI
Solid understanding of REST APIs and experience integrating third-party services and data sources
Ability to work autonomously in a fast-paced remote environment with minimal hand-holding
Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.
Experience with model fine-tuning, RLHF, or custom training workflows (Preferred)
Familiarity with MLOps tooling and model deployment pipelines (Docker, cloud functions, etc.) (Preferred)
Exposure to multimodal systems (vision, audio, or document understanding) (Preferred)
Background in data engineering or working with structured/unstructured data at scale (Preferred)
Contributions to open-source AI projects or a strong public portfolio of AI work (Preferred)

AI Engineer

Key skills