ML Engineer

The core responsibilities for the job include the following:

Scale Conversational AI in Production:

• Own and scale conversational AI pipelines that already live in production and push reliability, latency, and quality as traffic grows.
• Extend multi-turn conversations, context management, memory, and agentic workflows to handle massive concurrent scale.
• Improve grounding, consistency, and relevance under real production load, not just in demos.

Build and Scale Voice AI:

• Build and scale our real-time voice AI systems, the platform's newest and fastest-growing surface.
• Work across streaming ASR, TTS, turn-taking, and latency to make voice conversations feel instant and natural.
• Unify chat and voice under a single conversational and memory layer, so context carries across modalities.

LLM and RAG Systems at Scale:

• Build and scale retrieval over astrological knowledge, structured data, and user context across LLMs, embeddings, vector search, RAG, and tool calling.
• Push chunking, retrieval, metadata filtering, and ranking quality as the knowledge base and traffic grow.
• Work with closed- and open-source models, and evaluate when to bring capabilities in-house.

LLM Orchestration and Optimization, at Volume:

• Build and harden model orchestration and routing across LLMs by task, quality, latency, and cost, now running at real production volume.
• Build fallbacks, structured outputs, and model-specific workflows that hold up under scale.
• Own token usage, inference latency, and infra cost as first-class metrics, not afterthoughts.

Personalization and Memory, at Platform Scale:

• Build ML/AI systems that use user profiles, conversation history, preferences, goals, and astrological signals to personalize every interaction.
• Own and extend our personal intelligence and memory systems as the user base scales.
• Improve personalization, recommendations, and ranking so the platform gets sharper with every user, not just every release.

Astrology Intelligence:

• Work with structured astrological data and our astrology computation systems.
• Combine deterministic astrology logic, ML models, retrieval, and LLM reasoning to generate personalized insights.
• Build out Kundli, planetary influence, personality, life-area, timing, and recommendation systems.

Evaluation and Quality, in Production:

• Build evaluation datasets and automated tests for LLM and ML systems, covering both chat and voice.
• Measure quality, retrieval relevance, grounding, hallucination, consistency, and personalization, and catch regressions before users do.