• Build AI agents that automate labor-intensive clinical study workflows—like drafting clinical trial documentation • Extend and improve our agent framework (built on pydantic-ai) and the infrastructure that supports it • Build data pipelines that give our agents access to the research and context they need • Collaborate directly with study project managers, clinicians, and other stakeholders to understand problems and deliver solutions • Do whatever it takes to ship—including scrappy UI work when that's what's blocking progress

• Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion). • Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs. • End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring. • LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements. • AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines. • Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team. • Mentor junior engineers and contribute to technical direction and engineering best practices.

• Architect and prototype edge-AI pipelines enabling local training, inference, and cross-device collaboration. • Build developer-friendly APIs and SDKs that abstract distributed complexity into efficient, elegant experiences. • Optimize performance across constrained and diverse hardware—including GPUs, NPUs, and embedded accelerators. • Integrate edge-first data flows with privacy-preserving and verifiable computation frameworks. • Collaborate with product, research, and infrastructure teams to shape the developer experience for edge-native AI. • Mentor engineers and help build a culture focused on precision, performance, and trust.

Role Description We are looking to hire passionate Senior AI Engineers to help turn data into intelligent, production-ready solutions. You will work across the full AI stack: traditional machine-learning models, large language models (LLMs), computer-vision pipelines, and analytics / forecasting workflows. If you enjoy exploring data, building state-of-the-art models, and shipping reliable AI services, we would love to meet you. - Model Development: Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion). - Data Exploration & Analytics: Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs. - End-to-End Pipelines: Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring. - LLM & Agentic AI Engineering: Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements. - AI Automation & Integration: Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines. - Continuous Learning: Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team. - Mentor junior engineers and contribute to technical direction and engineering best practices. Qualifications - BSc in Computer Science, Mathematics, or related field. - 5+ years of professional experience working on AI/ML projects. - Good command of English (written and spoken). - Proficient in Python and core libraries (PyTorch / TensorFlow, scikit-learn, pandas, NumPy). - Solid understanding of machine-learning algorithms, deep-learning fundamentals, and basic statistics. - Experience with data wrangling and visualization (Matplotlib / Plotly) and exploratory analysis. - Familiarity with at least one of: OpenCV, Hugging Face Transformers, LangChain, MLflow, or similar. - Good grasp of software-engineering best practices: Git, code reviews, testing, CI. Requirements - Knowledge of C++ or C# for performance-critical modules. - Experience deploying models via Docker, Kubernetes, or cloud AI services. - Exposure to vector databases and RAG workflows. - Skill in BI / dashboard tools (Power BI, Tableau, Streamlit) or time-series frameworks (Prophet, statsmodels). - Familiarity with MLOps / LLMOps tooling (DVC, MLflow Tracking, Weights & Biases, BentoML). - Experience with image processing techniques (e.g., OpenCV, image segmentation, feature extraction). - Experience with Spark (PySpark) and distributed data processing, including usage of platforms such as Databricks, AWS EMR, or GCP Dataproc. - Strong SQL skills and experience working with large-scale datasets, including partitioning and performance tuning. - Familiarity with modern data lake architectures and scalable data storage concepts.