AI Engineer

As a Founding AI Engineer, you will play a pivotal role in shaping the foundation of our security platform. Working closely with our Head of RnD, you will be responsible for building the platform from scratch, refining product features, and ensuring technical requirements align with user needs. Additionally, you will contribute significantly to the development of our company's R&D culture.

• Develop and deploy machine learning models and algorithms to analyze large datasets and solve complex business problems.
• Collaborate with cross-functional teams to define project objectives, gather requirements and develop AI Solutions that meet business needs.
• Design and implement scalable and efficient data pipelines for preprocessing, cleaning, transforming, storing, etc.
• Work extensively with language models (LLMs), retrieval- augmented generation (RAG) and vector databases to enhance data-driven solutions.
• Experiment with different machine learning techniques and algorithms (e.g. , supervised learning, unsupervised learning, deep learning) to identify the most effective approaches to solving specific problems.
• Evaluate model performance and RAG pipeline performance, making recommendations for improvements or adjustments based on validation metrics and business requirements.

• 5+ years of experience in data science, machine learning, or AI engineering roles, preferably in a fast-paced industry or technology company.
• Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions and actionable insights.
• Proficiency in programming languages such as Python, R, or Scala and experience with libraries and frameworks for data manipulation, analysis and modeling (e.g., pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
• Solid understanding of machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, reinforcement learning, and natural language processing.
• Experience with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark, Dask). For processing and analyzing large-scale datasets.
• Experience with vector databases for efficient similarity search and data retrieval.
• Experience with tools like LlamalIndex or LangChain for integrating LLMs with data sources.
• Familiarity with MLOps practices for deploying and maintaining machine learning models in production environments.