AI/ML Engineer LLM, RAG & Machine Learning (Secunderabad)

About The Role 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. The ideal candidate will enjoy working close to data, models, and algorithms, and will have a passion for optimizing retrieval latency and agent decision-making paths. Experience in end-to-end ownership from data to deployment is essential. Key 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. Required Skills And Qualifications - 23 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. - Experience working with large datasets, Knowledge Graphs, SQL, and production ML systems. - Ability to build scalable data pipelines for processing unstructured datasets. Preferred Qualifications - Experience developing models using PyTorch or TensorFlow. - Familiarity with LLM fine-tuning techniques (e.g., LoRA, adapters). - Knowledge of MLOps practices and experiment tracking tools. Why Join Us - Work on real-world AI and LLM systems used in production. - Take end-to-end ownership of projects from data processing to deployment. - Cooperative 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 About The Role 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. The ideal candidate will enjoy working close to data, models, and algorithms, and will have a passion for optimizing retrieval latency and agent decision-making paths. Experience in end-to-end ownership from data to deployment is essential. Key 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. Required Skills And Qualifications - 23 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. - Experience working with large datasets, Knowledge Graphs, SQL, and production ML systems. - Ability to build scalable data pipelines for processing unstructured datasets. Preferred Qualifications - Experience developing models using PyTorch or TensorFlow. - Familiarity with LLM fine-tuning techniques (e.g., LoRA, adapters). - Knowledge of MLOps practices and experiment tracking tools. Why Join Us - Work on real-world AI