AI Engineer

For a Machine Learning Engineer, this means working on some of the hardest problems at the intersection of AI, personalisation, and human relationships: building models that understand users deeply, learn from long-term interactions, develop persistent context and deliver increasingly personalised conversations at scale. This is, in our opinion, the most exciting work in frontier AI today - figuring out how AI learns to know a person, and how that person can trust our AI more than anyone else in their world.

Responsibilities:

• Build and own production-grade ML/AI systems end-to-end.
• Develop LLM, RAG, conversational AI, and agentic workflows.
• Work on personalisation, memory, recommendations, and user intelligence.
• Build LLM orchestration, evaluation, and optimisation 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.
• Good to Have: LangChain/LangGraph, vector databases, Hugging Face/open-source LLMs, MLOps/LLM evaluation, recommendation systems, or Indic/multilingual NLP.