AI Engineer

MyNaksh; Bengaluru; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

MyNaksh in Bengaluru, India, is seeking a hands-on ML/GenAI engineer to build production-grade AI systems end-to-end, including LLM orchestration, RAG pipelines, and agentic workflows.

You will work on personalization, memory, recommendations, and user intelligence, collaborating with Product, Backend, and other teams to ship impactful features at scale with a focus on latency, cost, and quality.

Qualifications

• 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, and produce.

Responsibilities

• 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.

Skills

Python

ML Engineering

LLMs & RAG

Production Deployment

System Design

Cloud

End-to-End Ownership

Tools

LangChain

LangGraph

Vector databases

Hugging Face

Job description

Responsibilities:

• 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, and produce.
• Good to Have: LangChain/LangGraph, vector databases, Hugging Face/open-source LLMs, MLOps/LLM evaluation, recommendation systems, or Indic/multilingual NLP.