AI/ML Developer (Mid-Level) - NLP & Generative AI

Experience : 5+ Years

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. 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 :

- Hands-on experience in Machine Learning / Deep Learning development

- At least 2 or more 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