Confidential (via Talento HR)
Delhi NCR · On-site · Full-time · 5-10 years experience
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About the Role
We are looking for an AI/ML Developer with a strong focus on Natural Language Processing (NLP) and Generative AI to join our team and having experience of 5+ years.
You will design, build, and deploy ML/LLM-based solutions that power intelligent features across our products, including chatbots, semantic search, document understanding, and generative content applications.
Experience Required
• 1–3 years of hands-on experience in Machine Learning / Deep Learning development
• At least 1–2 years of specific experience working with NLP and/or LLM-based systems
Key Responsibilities
• Design, develop, and fine-tune NLP models for tasks such as text classification, NER, summarization, and question-answering
• Build and optimize applications using Large Language Models (LLMs), including prompt engineering, fine-tuning, and RAG (Retrieval-Augmented Generation) pipelines
• Develop and maintain vector search/embedding-based retrieval systems
• Collaborate with product and engineering teams to integrate ML/LLM capabilities into production applications
• Evaluate and benchmark model performance (accuracy, latency, cost, hallucination rate)
• Deploy and monitor models in production environments
• Stay current with the latest research and tools in NLP and Generative AI, and evaluate their applicability to business use cases
Required Skills
Programming & Core Technical
• Strong proficiency in Python
• Experience with SQL for data querying
• Solid understanding of data structures, algorithms, and object-oriented programming
NLP & LLM Specific
• Hands-on experience with Hugging Face Transformers
• Experience with LLMs (OpenAI, Anthropic Claude, Llama, Mistral, etc.) via APIs or self-hosted deployment
• Prompt engineering and prompt optimization techniques
• Experience fine-tuning LLMs (LoRA, QLoRA, PEFT) or training custom NLP models
• RAG (Retrieval-Augmented Generation) architecture and implementation
• Vector databases (Pinecone, FAISS, or similar)
• Text embeddings and semantic search
• Tokenization, text preprocessing, and language model evaluation metrics
ML/DL Frameworks & Libraries
• PyTorch and/or TensorFlow
• Scikit-learn
• Pandas, NumPy
• LangChain, LlamaIndex, or similar LLM orchestration frameworks
ML Fundamentals
• Supervised/unsupervised learning, classification, clustering
• Model evaluation and validation techniques
• Feature engineering
• Understanding of overfitting, bias-variance tradeoff, and regularization
MLOps & Deployment
• Experience deploying models via REST APIs (FastAPI/Flask)
• Containerization with Docker
• Cloud platforms — AWS, GCP, or Azure
• Model versioning and experiment tracking (MLflow, Weights & Biases, DVC)
Data Handling
• Data cleaning, preprocessing, and pipeline development
• Experience working with large-scale unstructured text datasets
• Familiarity with distributed data processing (Spark or Dask) is a plus
Tools & Version Control
• Git/GitHub or GitLab
• Jupyter Notebooks
• Familiarity with Agile/Scrum development practices