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.

Skills
Agentic AI, Generative AI, LangChain, Machine Learning, MLOps, NLP, Python, Vector Databases

Experience
3-6 yrs