Senior AI Engineer

Sola Security is looking for an experienced AI Engineer.

Key Responsibilities

• 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.