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.
• Research-to-Production Mindset: You enjoy taking promising AI ideas from experimentation to reliable production systems.
• AI-first thinking: You naturally look for ways modern AI can solve meaningful product and engineering problems.
• Strong ownership: You take responsibility for outcomes, from problem formulation through deployment and iteration.
• Technical depth: You can go deep into models, experiments, data, evaluation, and production behavior when required.
• Product mindset: You understand the business and user impact behind AI decisions.
• Bias for experimentation: You are comfortable testing hypotheses quickly while maintaining scientific rigor.
• Collaboration: You can work effectively with engineering, product, and design teams to turn AI capabilities into products.

AI-First Mindset:

• We are particularly interested in scientists who actively explore and apply modern AI techniques to real-world product and engineering problems.
• Experience with any of the following would be highly valuable: LLMs / Generative AI; Transformer architectures and model fine-tuning; OpenAI, Anthropic, Gemini, or similar model APIs; RAG and vector databases AI agents and agentic workflows; prompt engineering and structured evaluation; LangChain / LangGraph or similar AI orchestration frameworks model evaluation, observability, reliability, and cost optimization; building AI-native products or intelligent features.
• Strong research depth is valuable, but the role requires the ability to translate research and experimentation into practical AI capabilities that can be deployed and used in production.

Good to Have:

• Experience building AI-native or AI-powered SaaS products.
• Experience with LLM evaluation, fine-tuning, alignment, or inference optimization.
• Experience with PyTorch, TensorFlow, scikit-learn, or similar ML frameworks.
• Experience with AWS/GCP/Azure and cloud-based ML infrastructure.
• Experience with ML pipelines, model serving, and MLOps practices.
• Experience with PostgreSQL, Redis, MongoDB, vector databases, or similar data systems.
• Experience with Docker and Kubernetes.
• Experience with distributed systems and scalable AI applications.
• Research publications, open-source contributions, patents, or a strong technical portfolio.
• Experience working in fast-paced startup/product environments.

Tech Stack:

• Programming: Python AI / ML: Machine Learning, Deep Learning, NLP, Generative AI, LLMs.
• AI Systems: RAG, AI Agents, Model Evaluation, AI APIs.
• Engineering: APIs, Cloud, Model Deployment, Data Pipelines, Production AI Systems.