Machine Learning Engineer - Large Language Model (LLM) and Retrieval-Augmented Generation (RAG)
Remote
AI Summary
Design and implement end-to-end RAG solutions, optimize LLM inference performance, and deploy scalable infrastructure using Docker and orchestration platforms.
Key Highlights
Design and implement end-to-end RAG solutions
Optimize LLM inference performance
Deploy scalable infrastructure using Docker and orchestration platforms
Collaborate with Data Scientists and product teams
Integrate and tune vector search stacks
Technical Skills Required
Benefits & Perks
Fully remote role with flexible hours
Opportunity to work on cutting-edge LLM/RAG products
Collaborative, fast-paced engineering culture
Job Description
Machine Learning Engineer – LLM & RAG (Remote, India)
About The Opportunity
We operate in the AI/ML and Enterprise Software sector, building production-ready large language model (LLM) applications and retrieval-augmented generation (RAG) systems that solve real-world enterprise problems. The team focuses on scalable, low-latency LLM inference, vector search, and data pipelines to deliver intelligent search, summarization, and automated knowledge workflows for customers across industries.
Role & Responsibilities
- Design and implement end-to-end RAG solutions: document ingestion, embedding generation, vector indexing, retriever design, and LLM-based response generation.
- Develop and maintain Python back-end services and APIs that integrate LLMs, LangChain/LlamaIndex workflows, and vector search for production use.
- Optimize LLM inference performance: model selection, batching, quantization, ONNX/Triton integration, and memory/GPU optimization to meet latency and cost SLAs.
- Integrate and tune vector search stacks (FAISS, Milvus, Weaviate, or hosted vector DBs) and design embedding strategies for robust retrieval.
- Deploy and operate scalable infrastructure using Docker and orchestration platforms; automate CI/CD, monitoring, and alerting for ML services.
- Collaborate with Data Scientists and product teams to productionize models, implement A/B experiments, monitor drift, and iterate on model quality and UX.
Must-Have
- 4+ years of experience in machine learning or ML engineering with hands-on LLM projects.
- Strong software engineering in Python and building production back-end services.
- Experience with transformer frameworks and LLM tooling (Hugging Face Transformers, PyTorch).
- Practical experience building RAG pipelines and working with vector search (FAISS or similar).
- Proven experience deploying ML services with Docker and cloud environments (AWS/GCP/Azure).
- Knowledge of model optimization and serving techniques (quantization, ONNX, Triton, batching).
- Hands-on experience with LangChain, LlamaIndex, or similar orchestration frameworks.
- Familiarity with vector databases (Milvus, Weaviate) and managed vector DB services.
- Experience with MLOps and monitoring tools (MLflow, Prometheus, Grafana, model-drift tooling).
- Fully remote role with flexible hours supporting work-life balance across India.
- Opportunity to work on cutting-edge LLM/RAG products and influence architecture and tooling choices.
- Collaborative, fast-paced engineering culture that values ownership, experimentation, and scalable design.
Skills: llm,rag,python