Data Scientist / Machine Learning Engineer (LLM & RAG Systems)

Lynceus; United Kingdom; On-site; GBP 40,000 - 80,000; Full time

Job description

What you will do

• Develop and optimize RAG pipelines to enhance LLM-driven troubleshooting.
• Work with PyVector to store and retrieve relevant data efficiently.
• Implement vector embedding models to power retrieval and improve accuracy.
• Build high-quality Python code to power our AI applications.
• Deploy and scale models on AWS (S3, Lambda, ECS, SageMaker, Bedrock).
• Fine-tune and evaluate LLMs for task-specific reasoning and accuracy.
• Implement efficient document indexing and retrieval for knowledge-intensive queries.
• Experiment, iterate, and deploy features quickly based on real customer feedback.
• Work with a small but highly skilled team to push the limits of industrial AI.
• Take ownership of challenges, figure things out as you go, and adapt to changing priorities.

What we are looking for

• Strong Python development skills, with experience in machine learning or NLP.
• Experience with RAG architectures and knowledge retrieval techniques.
• Familiarity with PyVector for vector search and retrieval.
• Experience using vector embedding models (e.g., OpenAI, Bedrock Titan, Cohere, SentenceTransformers).
• Understanding of embedding models and best practices for retrieval.
• Experience working with AWS for cloud-based ML deployment.
• Knowledge of LangChain, LlamaIndex, or other RAG frameworks.
• Ability to work in a fast-paced, experimental environment, where iteration and adaptation are key.
• A self-starter who can work autonomously in a remote setting.
• A strong problem-solver who thrives when thrown in at the deep end.

Why us?

• Work on cutting-edge AI in a complex and impactful industry.
• Be part of a small, agile, and highly talented team where your work truly matters.
• Enjoy full remote flexibility with the autonomy to shape your role.
• Build a product that solves real-world problems for top-tier industrial clients.

Our Values

• We are owners: We get involved, understand the impacts we make and deliver what we commit to.
• We are bias for action: We take actions with a goal in mind and act fast.
• We believe there's always something to learn: We are not afraid of asking questions and learning from different people all the time.
• We collaborate and communicate: We understand that teamwork is key and the team's success is also my success.
• We support and respect each other: We support both external and internal stakeholders' needs and respect differences we all have.
• We stand by our values and walk the talk: We stand by our values to make sure that our culture is strong despite being remote-first.

Interview Process

• Screening Interview with our HR Team.
• Technical Interview with our Delivery Team.
• Cultural Fit Interview with our Co-Founder.