๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฐ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฏ๐ฌ-๐ฐ๐ฌ ๐๐ฃ๐)
Experience: 3+ yrs
Location: Bengaluru
We are looking for a skilled and innovativeย AI/ML Engineerย to build and scale intelligent AI systems focused onย Large Language Models (LLMs), conversational AI, long-term memory, and personalization. The role involves developing advanced retrieval systems, adapting open-source models, and building domain-specific AI capabilities that deliver context-aware, accurate, and personalized user experiences.
The ideal candidate will combine strong Python programming skills with hands-on expertise in LLM engineering, Retrieval-Augmented Generation (RAG), vector databases, model fine-tuning, and production deployment.
Requirements
Key Responsibilities
- Design and develop conversational AI systems with contextual understanding, long-term memory, and personalised response generation.
- Build and optimiseย RAG pipelinesย using embeddings, vector databases, structured data, and knowledge retrieval techniques.
- Develop memory systems for user profiling, information extraction, relevance scoring, retrieval, ranking, and context selection.
- Integrate, deploy, and fine-tune open-source LLMs for specific product and domain requirements.
- Develop specialised Small Language Models (SLMs) to support domain-specific reasoning and improve response quality.
- Create domain-specific datasets and training pipelines for fine-tuning, model adaptation, and continuous learning.
- Implement LLM orchestration, prompt engineering, classification, ranking, and lightweight machine learning workflows.
- Build evaluation frameworks to measure retrieval relevance, memory accuracy, reasoning quality, response consistency, and overall model performance.
- Optimise inference latency, compute utilisation, model reliability, and deployment costs.
- Develop and maintain model-serving APIs, automated tests, monitoring systems, and production deployment workflows.
- Investigate AI failures, analyse model outputs, and implement improvements based on measurable performance indicators.
- Collaborate with Product and Engineering teams to translate user requirements into scalable AI solutions.
- Evaluate emerging LLM technologies, retrieval architectures, and model optimisation techniques to improve system capabilities.
What Makes You a Great Fit
- 3+ years of experienceย in AI/ML engineering, machine learning, NLP, or production AI application development.
- Strong proficiency inย Pythonย and experience building reliable, production-grade AI systems.
- Hands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG architectures.
- Understanding of LLM fine-tuning, dataset preparation, model evaluation, and inference optimisation.
- Experience developing retrieval, conversational memory, context management, or personalisation systems.
- Familiarity with LLM orchestration frameworks, prompt engineering, and multi-step AI pipelines.
- Experience deploying models through APIs and implementing testing, monitoring, and production support.
- Strong analytical and problem-solving skills, with the ability to evaluate AI improvements using measurable outcomes.
- Knowledge ofย LoRA/QLoRA, SLMs, knowledge graphs, Neo4j, or graph-based retrievalย is an advantage.
- Experience with recommendation systems, ranking models, synthetic data generation, or automated evaluation pipelines is beneficial.
- Familiarity with GPU inference optimisation and self-hosted open-source models is desirable.
- Experience in astrology, symbolic reasoning, or specialised domain-reasoning systems is an added advantage.
- Strong ownership, communication, and collaboration skills, with the ability to drive AI initiatives from experimentation through production.