ML Engineer

MyNaksh; Bengaluru; On-site; INR 450,000 - 750,000; Full time

Job summary

MyNaksh in Bengaluru, India seeks an AI/ML Engineer to design, build and operate production-grade AI systems with a focus on LLMs, retrieval, memory, and personalization. You will own end-to-end features, from design to deployment and monitoring, delivering reliable AI capabilities at scale.

The role requires strong Python, software fundamentals, and hands-on experience with deploying AI applications using LLMs, NLP, and RAG techniques, with an emphasis on measurable quality and efficiency.

Qualifications

• 3+ years of experience in software engineering or AI/ML engineering.
• Strong Python programming and software fundamentals including API design.
• Hands-on experience building and deploying AI/ML applications with LLMs/NLP.
• Practical understanding of RAG, embeddings, search or context-selection systems.
• Experience building backend services and deploying AI capabilities.
• Understanding evaluation methods for AI systems and regression detection.
• Experience with production deployment, Docker, and cloud infrastructure like AWS.
• Ability to design scalable, reliable systems balancing latency and cost.
• Ownership mindset to take a feature from problem to deployment.

Responsibilities

• Build production-ready conversational AI experiences using LLMs, structured context, and user-specific information.
• Improve context selection, follow-up handling, and response consistency across conversations.
• Design and improve retrieval-augmented generation pipelines using embeddings and vector search.
• Build long-term memory and user-profile systems for retrieval and personalization.
• Design multi-step AI pipelines for understanding, retrieval, reasoning, and generation.
• Optimize model selection, prompts, tool calls, and pipeline execution.
• Reduce unnecessary LLM calls while balancing quality, latency, and cost.
• Implement fallbacks, retries, timeouts, and graceful degradation for production reliability.
• Build clean Python services and APIs for AI features.
• Take features end-to-end from design to testing, deployment, and monitoring.
• Collaborate with product and engineering teams to translate needs into reliable AI capabilities.

Skills

Python

Software engineering

LLMs

NLP

Production systems

System design

Docker

AWS

Tools

Docker

AWS

LangChain

Job description

We are looking for an AI/ML Engineer (SDE2) who can design, build, and operate production-grade AI systems, with a strong focus on LLM applications, retrieval, memory, and personalization. This role is suited to an engineer who combines strong software engineering fundamentals with hands-on experience building real-world AI applications, not just notebooks, model experiments, or basic LLM API integrations.

The core responsibilities for the job include the following:

Build Conversational AI Systems:

• Develop production-ready conversational AI experiences using LLMs, structured context, and user-specific information. Build systems that maintain conversational context, retrieve relevant past information, and generate personalized responses.
• Improve context selection, follow-up handling, and response consistency across conversations.

Build RAG, Retrieval, and Memory Systems:

• Design and improve retrieval-augmented generation (RAG) pipelines using structured data, embeddings, vector search, and graph-based retrieval where appropriate.
• Build long-term memory and user-profile systems that extract, store, update, rank, and retrieve relevant information.
• Handle conflicting information, memory freshness, relevance scoring, deduplication, and context selection.
• Improve retrieval quality, relevance, and personalization through measurable evaluation.

Develop LLM Orchestration and Optimization:

• Design multi-step AI pipelines involving query understanding, retrieval, reasoning, and response generation.
• Optimize model selection, prompt design, tool calls, and pipeline execution.
• Reduce unnecessary LLM calls while balancing quality, latency, and cost.
• Implement fallbacks, retries, timeouts, and graceful degradation for production reliability.

Build Personalization and User Intelligence:

• Develop systems that adapt to user preferences, goals, interests, historical interactions, and evolving context.
• Combine structured user profiles, conversational memory, and retrieved knowledge to improve relevance.
• Explore lightweight ML approaches for classification, ranking, recommendation, and context selection where they outperform additional LLM calls.

Evaluate and Improve AI Quality:

• Design evaluation datasets and test cases for retrieval quality, memory accuracy, personalization, and response consistency.
• Measure quality using appropriate offline evaluations and production metrics.
• Build regression tests to catch failures in prompts, retrieval, memory updates, and model behavior.
• Use evaluation results and production feedback to drive improvements.

Own Production AI Engineering:

• Build clean, maintainable Python services and APIs for AI features.
• Take features end-to-end: design, implementation, testing, deployment, monitoring, and iteration.
• Instrument latency, token usage, model costs, errors, and quality-related metrics.
• Debug production failures and improve scalability, security, and operational reliability.
• Collaborate with product and engineering teams to translate user needs into reliable AI capabilities

Requirements:

• 3+ years of experience in software engineering, AI/ML engineering, applied ML, or a closely related role.
• Strong Python programming and software engineering fundamentals, including clean code, API design, debugging, testing, and maintainable architecture.
• Hands-on experience building and deploying AI/ML applications using LLMs, generative AI, NLP, or related technologies.
• Practical understanding of RAG, retrieval, embeddings, search, or context-selection systems.
• Experience building backend services and integrating AI capabilities into real applications.
• Understanding of evaluation methods for AI systems, including how to measure quality and detect regressions.
• Experience with production deployment, Docker, and cloud infrastructure such as AWS or an equivalent platform.
• Ability to reason about system design, latency, cost, reliability, and failure handling.
• Ownership mindset: able to independently take a feature from problem definition through implementation, testing, and deployment.

Good to Have:

• Experience with vector databases or search systems such as Qdrant, pgvector, or similar technologies.
• Experience with graph databases such as Neo4j, knowledge graphs, or graph-based retrieval.
• Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or comparable tools.
• Experience with Redis, PostgreSQL, S3, background jobs, queues, and asynchronous processing.
• Familiarity with Hugging Face, model serving, vLLM, fine-tuning, or model optimization.
• Experience with recommendation systems, ranking, user modeling, or personalization.
• Experience with observability and evaluation tools such as Langfuse, tracing platforms, or equivalent solutions.
• Experience with multilingual NLP, Indic languages, or domain-specific knowledge systems.
• Interest in astrology, behavioral personalization, or building AI products that improve with continued user interaction.

What Success Looks Like:

• Ship reliable AI features that are used in the MyNaksh product.
• Improve memory, retrieval, and personalization quality using measurable metrics.
• Reduce unnecessary model calls, inference costs, and response latency without compromising quality.
• Build repeatable evaluation and regression-testing workflows for AI features.
• Take ownership of production systems, identify failure modes, and continuously improve reliability and performance.