AI/ML Engineer – LLM, RAG & Machine Learning

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

• 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.
• 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.
• 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