AI Engineer

🕒 March 18

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Lifebit

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 2017

☁️ SaaS

🧬 Biotechnology

🤖 Artificial Intelligence

SaaS • Biotechnology • Artificial Intelligence

Lifebit is a federated data intelligence platform that enables secure, privacy-preserving analysis of sensitive health and genomics data across institutions, clouds, and borders. Its products include a Trusted Data Lakehouse, Trusted Research Environment, Trusted TargetID and other modules for data harmonization (OMOP/FHIR), cohort building, multi-omics ETLs, and AI-powered target identification and surveillance. Lifebit is delivered as an enterprise SaaS offering that integrates with clinical systems (Epic, Cerner, Meditech), supports common data science tools (Jupyter, RStudio, Nextflow), and is used by pharmaceutical companies, public-sector genomics initiatives, health systems and research organizations.

📋 Description

• Design and implement autonomous AI agents using frameworks like LangGraph, CrewAI, or AutoGen to handle complex, multi-step scientific queries. • Develop sophisticated reasoning loops (e.g., ReAct, Plan-and-Execute) that allow agents to decompose high-level research goals into actionable sub-tasks. • Build and optimize Advanced RAG (Retrieval-Augmented Generation) pipelines that integrate structured clinical data and unstructured scientific literature. • Create and maintain 'tools' for AI agents, enabling them to safely interface with Lifebit’s federated APIs, SQL databases, and bioinformatic execution engines. • Implement secure, sandboxed code-interpreter capabilities, allowing agents to write and execute Python or R code for data visualization and statistical analysis. • Fine-tune LLMs for specific function-calling and tool-use accuracy within the life sciences domain. • Develop robust evaluation frameworks (LLM-as-a-judge) to measure agentic performance, truthfulness, and safety in a clinical context. • Implement 'Human-in-the-loop' (HITL) patterns to ensure high-stakes scientific decisions are always reviewed by domain experts. • Partner with Security teams to ensure agents operate within strict data privacy boundaries, preventing prompt injection or unauthorized data egress in federated nodes. • Work with Product and UX teams to design intuitive interfaces for interacting with agentic systems (e.g., conversational research assistants). • Scale agentic workloads in production using Kubernetes, ensuring low-latency reasoning and efficient token usage.

🎯 Requirements

• Education: BSc/MSc in Computer Science, Artificial Intelligence, Machine Learning, or a highly quantitative field (PhD preferred). • Experience: 2+ years of hands-on experience as an AI or ML Engineer, building a validated real product, ideally within a product-led biotech, health-tech, or SaaS company. • Technical Stack: Deep proficiency in Python and Typescript and standard ML frameworks (e.g., Langfuse, PyTorch , TensorFlow, JAX, Scikit-learn). • NLP/LLM Expertise: Proven experience working with Large Language Models, including fine-tuning, and RAG (Retrieval-Augmented Generation) architectures. • Cloud & Infrastructure: Familiarity with AWS/Azure/GCP and experience deploying models in Docker/Kubernetes environments. • Domain Knowledge: It is a plus to have experience working with biological, genomic, or clinical data is a significant advantage. • Autonomy: A self-starter mindset with the ability to navigate ambiguity and drive AI projects from concept to production without constant oversight.

🏖️ Benefits

• Compensation: Your work is rewarded with a competitive salary and performance-based incentives. • Professional Development: You are granted an annual personal development budget of £1,000 and access to leading industry conferences, training, and certifications. • Flexible Working: Receive 21-25 days of annual leave and fully remote work to maintain a healthy work-life balance. • Diverse Team Culture: Join an international and diverse team passionate about transforming healthcare through data. • Deep Technology & Science: Get exposure to problems and applications in the cloud, data analysis, ML, life sciences, and big data fields.