Machine Learning Engineer - 2

📋 Description Build and own production-grade ML/AI systems end-to-end. Develop LLM, RAG, conversational AI, and agentic workflows. Work on personalization, memory, recommendations, and user intelligence. Build LLM orchestration, evaluation, and optimization systems for quality, latency, and cost. Combine structured astrology intelligence with ML, retrieval, and LLM reasoning. Work closely with Product, Backend, and other teams to ship impactful features at scale. 🎯 Requirements 3–5 years in ML, Applied ML, NLP, or GenAI engineering. Strong Python and software engineering fundamentals. Hands-on experience with LLMs, RAG, embeddings, vector search, or conversational AI. Experience deploying ML/AI systems to production. Strong understanding of ML fundamentals, evaluation, APIs, system design, scalability, and cloud. Ability to own problems end-to-end: design → build → evaluate → production.