Browse Jobs
Explore open roles across India.Explore active software roles. Sign up to apply and get AI match scores.
Top MatchesTop Job Matches
Loading jobs…
Back to Browse Jobs
Yathi Solutions
AI/ML Engineer – LLM, RAG & Machine Learning
Yathi Solutions
Hyderabad•On-site•2-5 Yrs•Jun 29
Job description
Overview
We are seeking a skilled AI/ML Engineer to design, build, and deploy production-grade AI systems powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and core Machine Learning.
Responsibilities
- Design, build, and optimize RAG pipelines (chunking, embeddings, vector search, re-ranking, and evaluators) with a focus on low latency.
- Build robust data pipelines to ingest, clean, and chunk large-scale unstructured data (PDFs, HTML, logs).
- Improve LLM response quality, grounding, and hallucination reduction.
- Web search data retrieval for LLMs
- External data ingestion and management for AI systems
- Small Language Models (SLMs) development
- Data pipelines for external sources (APIs, web scraping, streaming data)
- Model inference optimization and latency reduction
- Train and evaluate machine learning models, including classification, regression, and clustering.
- Develop scalable architectures for agent tool-calling and SQL integration.
- Collaborate on improving HNSW, semantic ranking, and recall metrics.
- Deploy and optimize AI systems for low-latency and cost-efficient inference.
Qualifications
- 2–3 years of relevant experience in AI/ML engineering.
- Proven experience in Python and proficiency with frameworks like LangChain, LlamaIndex, Milvus, Types of RAGs and GenAI libraries for Agentic AI & orchestration.
- Hands-on experience deploying, optimizing, and fine-tuning open-source models (e.g., Llama 3, Deepseek, Mixtral) using HuggingFace Transformers and vLLM.
- Strong problem-solving skills with a focus on optimizing retrieval latency.
- Ability to build scalable data pipelines for processing unstructured datasets.
- Familiarity with LLM fine-tuning techniques (e.g., LoRA, adapters).
- Knowledge of MLOps practices and experiment tracking tools.
- Work on real-world AI and LLM systems used in production.
- Take end-to-end ownership of projects from data processing to deployment.
- Collaborative environment focused on building scalable RAG and LLM architectures.
- Strong technical ownership and culture of growth.
- Career growth opportunities
- Opportunity to mentor and be mentored