AI/ML Engineer

AI/ML Engineer

About the role

AI/ML Engineer — LLMs, Memory & Personalization

Role Overview

We are looking for an AI/ML Engineer to build and evolve MyNaksh's AI intelligence layer, focusing on conversational AI, long-term memory, personalization, and domain-specific model development for astrological reasoning.

Key Responsibilities

• Build conversational AI systems with contextual understanding, long-term memory, and personalized responses.

• Design and optimize RAG pipelines using embeddings, vector search, structured data, and knowledge graphs.

• Develop systems for user profiling, memory extraction, retrieval, ranking, and context selection.

• Integrate, deploy, and fine-tune open-source LLMs and develop specialized SLMs for astrological reasoning.

• Build domain-specific training datasets and pipelines for fine-tuning, model adaptation, and continuous improvement.

• Develop LLM orchestration and lightweight ML systems for classification, ranking, and personalization.

• Build evaluation frameworks to measure retrieval quality, memory accuracy, astrological reasoning, and response consistency.

• Optimize model performance, inference costs, latency, and reliability in production.

• Own end-to-end AI engineering, from experimentation and prototyping to deployment, monitoring, and iteration.

• Required Skills & Experience

• Strong Python programming and experience building production-grade ML or AI systems.

• Hands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG.

• Understanding of LLM fine-tuning, model evaluation, dataset preparation, and inference optimization.

• Experience building retrieval, memory, or personalization systems.

• Familiarity with LLM orchestration, prompt engineering, and multi-step AI pipelines.

• Experience with model serving, APIs, testing, monitoring, and production deployment.

• Strong problem-solving skills and the ability to evaluate AI improvements using measurable outcomes.

• Good to Have

• Experience fine-tuning or deploying small language models (SLMs) using techniques such as LoRA/QLoRA.

• Experience with knowledge graphs, graph-based retrieval, or Neo4j.

• Experience with synthetic data generation, domain-specific training datasets, and automated evaluation pipelines.

• Experience in recommendation systems, ranking models, or lightweight ML classifiers.

• Familiarity with GPU inference optimization and self-hosted open-source models.

• Experience in astrology, symbolic reasoning, or other specialized domain-reasoning systems.

• What You'll Build at MyNaksh

• Personalized AI experiences powered by conversational context and long-term user memory.

• Retrieval and intelligence systems that make responses more relevant and consistent over time.

• Specialized models and evaluation pipelines that improve astrological reasoning using domain-specific data.

• Scalable AI infrastructure that balances response quality, latency, and cost.

• Must-have skills

• Machine LearningConversational AI

• Good-to-have skills

• harness design