Must-Have Skills & Traits Core Engineering Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django Strong understanding of software architecture, including RESTful API design, modularity, testing, and versioning. Experience working with databases (SQL/NoSQL), caching layers, and background job queues. AI/ML & GenAI Expertise Hands-on experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search Comfortable working with unstructured data (text, images) in real-world product environments Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate Ownership & Execution Demonstrated ability to take full ownership of features or modules from architecture to delivery Able to work independently in ambiguous situations and drive solutions with minimal guidance Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance Nice-to-Have Skills Experience in startup or fast-paced product environments. 2-5 years of relevant experience. Familiarity with asynchronous programming patterns in Python. Exposure to event-driven architecture and tools such as Kafka, RabbitMQ, or AWS EventBridge Data science exposure: exploratory data analysis (EDA), statistical modeling, or experimentation Built or contributed to agentic systems, ML/AI pipelines, or intelligent automation tools Understanding of MLOps: model deployment, monitoring, drift detection, or retraining pipelines Frontend familiarity (React, Tailwind) for prototyping or contributing to full-stack features Show more Show less

Must-Have Skills & Traits Core Engineering Advanced Python skills with a strong grasp of clean, modular, and maintainable code practices Experience building production-ready backend services using frameworks like FastAPI, Flask, or Django Strong understanding of software architecture, including RESTful API design, modularity, testing, and versioning. Experience working with databases (SQL/NoSQL), caching layers, and background job queues. AI/ML & GenAI Expertise Hands-on experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment Practical experience with LLMs and GenAI tools such as OpenAI APIs, Hugging Face, LangChain, or Transformers Understanding of how to integrate LLMs into applications through prompt engineering, retrieval-augmented generation (RAG), and vector search Comfortable working with unstructured data (text, images) in real-world product environments Bonus: experience with model fine-tuning, evaluation metrics, or vector databases like FAISS, Pinecone, or Weaviate Ownership & Execution Demonstrated ability to take full ownership of features or modules from architecture to delivery Able to work independently in ambiguous situations and drive solutions with minimal guidance Experience collaborating cross-functionally with designers, PMs, and other engineers to deliver user-focused solutions Strong debugging, systems thinking, and decision-making skills with an eye toward scalability and performance Nice-to-Have Skills Experience in startup or fast-paced product environments. 2-5 years of relevant experience. Familiarity with asynchronous programming patterns in Python. Exposure to event-driven architecture and tools such as Kafka, RabbitMQ, or AWS EventBridge Data science exposure: exploratory data analysis (EDA), statistical modeling, or experimentation Built or contributed to agentic systems, ML/AI pipelines, or intelligent automation tools Understanding of MLOps: model deployment, monitoring, drift detection, or retraining pipelines Frontend familiarity (React, Tailwind) for prototyping or contributing to full-stack features Show more Show less