Job Description

Key Responsibilities:

Design, train, and fine-tune LLMs for specific business applications using open-source and proprietary models. Build and scale NLP pipelines for text generation, summarization, classification, Q&A, and semantic search. Develop and implement Retrieval-Augmented Generation (RAG) systems using vector databases. Integrate LLM-based solutions into production environments ensuring scalability, performance, and reliability. Optimize inference performance of large models for real-time applications. Collaborate with data scientists, product managers, and backend engineers to deliver intelligent features end-to-end. Leverage prompt engineering and custom adapters (LoRA, PEFT, etc.) for model tuning and efficiency. Maintain reproducible ML workflows using MLOps tools and CI/CD practices.

Required Skills & Qualifications:

5+ years of total experience in software/ML engineering with at least 2 years in LLMs/NLP applications. Proficiency in Python and hands-on experience with ML/DL frameworks such as PyTorch or TensorFlow. Deep understanding of transformer architectures and modern LLMs (GPT, LLaMA, Claude, Gemini, etc.). Experience with prompt engineering, fine-tuning, or instruction tuning. Practical experience with RAG pipelines, embedding models, and vector databases like FAISS, Pinecone, or Weaviate. Experience deploying LLM applications in production at scale (preferably on cloud platforms like AWS, Azure, or GCP).
Job Type: Full-time

Pay: ₹476,387.04 - ₹1,689,471.78 per year

Work Location: In person