Machine Learning Engineer (NLP / Large Language Models)

Job Title: Machine Learning Engineer (NLP / Large Language Models)

Location: Bangalore / Hyderabad / Gurgaon (On-site / Hybrid)

Experience: 3 – 7 Years

Compensation: Up to ₹30,00,000 LPA

Employment Type: Full-Time

About the Role: We are seeking an experienced Machine Learning Engineer with specialized expertise in Neural Language Processing (NLP) to build, fine-tune, and deploy advanced language models. You will be instrumental in designing systems that understand, process, and generate human language at scale, powering core intelligent features across our product ecosystem.

Key Responsibilities

• Design, train, and deploy advanced neural network architectures for text classification, Named Entity Recognition (NER), semantic search, and generative tasks.
• Fine-tune open-weight Large Language Models (LLMs) and integrate foundational models via APIs (e.g., Anthropic, OpenAI) to solve complex domain-specific language problems.
• Build and optimize scalable Retrieval-Augmented Generation (RAG) pipelines and vector search systems.
• Pre-process, clean, and manage massive unstructured text datasets for model training and evaluation.
• Optimize ML models for latency and throughput in production environments using inference frameworks (ONNX, TensorRT, vLLM).
• Collaborate with backend engineering teams to seamlessly integrate NLP capabilities into robust microservices.

Must-Have Qualifications

• 3 to 7 years of dedicated experience in Machine Learning, with a heavy focus on NLP and deep learning.
• Expert-level programming skills in Python and foundational ML frameworks (PyTorch or TensorFlow).
• Deep theoretical and practical understanding of Transformer architectures, attention mechanisms, and word embeddings.
• Hands-on experience with NLP libraries and ecosystems (Hugging Face Transformers, spaCy, NLTK).
• Experience putting machine learning models into production and monitoring their performance/drift.

Good-to-Have Skills

• Experience deploying ML workloads using AWS Bedrock, SageMaker, or equivalent managed cloud AI services.
• Familiarity with vector databases (Pinecone, Qdrant, Milvus).
• Exposure to MLOps pipelines, Docker, and Kubernetes for model serving. Skills: machine learning,vector,learning,nlp,advanced,ml