Applied Scientist

Responsibilities:

• Design, develop, evaluate, and productionize machine learning and AI solutions for Kily's products.
• Develop and improve AI/ML models and intelligent systems using Python and modern machine learning frameworks.
• Work on generative AI and LLM-based capabilities, including model experimentation, evaluation, and application development.
• Build and improve AI-powered features, intelligent workflows, and AI-native product capabilities.
• Design experiments and evaluation frameworks to measure model quality, reliability, performance, and business impact.
• Work with large and complex datasets to develop robust features, training pipelines, and model evaluation methodologies.
• Apply machine learning, deep learning, NLP, and statistical techniques to solve real-world product problems.
• Collaborate closely with Engineering, Product, and Design teams to translate product requirements into effective AI solutions.
• Drive AI initiatives from research and prototyping through production deployment and continuous optimization.
• Investigate model failures, identify technical bottlenecks, and improve accuracy, latency, reliability, and cost.
• Stay current with emerging AI research, models, techniques, and tooling and evaluate their applicability to Kily's products.
• Document experiments, methodologies, findings, and technical decisions and communicate them clearly to cross-functional teams.

Requirements:

• Strong experience as an applied scientist, machine learning scientist, ML engineer, research engineer, or equivalent AI/ML role.
• Strong hands-on expertise in Python.
• Strong understanding of machine learning fundamentals, statistical methods, model development, and evaluation.
• Strong experience with deep learning and modern AI/ML techniques.
• Strong understanding of NLP and Transformer-based architectures.
• Hands-on experience building, training, fine-tuning, evaluating, and deploying machine learning or AI models.
• Strong understanding of generative AI and large language models.
• Experience designing production-ready AI systems rather than only research prototypes.
• Strong understanding of experimentation, offline/online evaluation, model performance, and reliability.
• Experience working with APIs, data pipelines, model-serving systems, and production AI workflows.
• Strong problem-solving and analytical skills.
• Excellent communication and ability to work closely with engineering and product teams.