Mode of Interview – F2F

Loc – Sec-62, Noida

Shift Timings - 2:30 PM - 11:30 PM IST

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

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

- Solid 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

If you are interested, please reply with your updated CV at [email protected] along with the following details:

- Current CTC:
- Expected CTC:
- Notice Period:
- Total Experience:
- Relevant Experience:
- Current Location:
- Preferred Location:

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