Description

You will design, build, and deploy LLM-based solutions, focusing on NLP and Generative AI use cases like classification, summarization, and RAG.

This role is hybrid.

Responsibilities

• Design and fine-tune NLP/LLM models for classification, summarization, and Q&A business use cases.
• Build RAG applications using embeddings, vector databases, and prompt engineering techniques.
• Develop efficient, production-quality Python code for training, inference, and evaluation pipelines.
• Integrate LLM applications into services and APIs, ensuring performance, reliability, and scalability.
• Establish model evaluation, monitoring, and governance practices including quality, safety, and bias checks.

Required Skills

• 6+ years of overall software development experience with a focus on AI/ML engineering.
• 2+ years of hands-on experience with deep learning for NLP and GenAI.
• Strong proficiency in Python, including writing testable and maintainable code.
• Experience with deep learning frameworks: PyTorch or TensorFlow.
• Experience with Hugging Face Transformers.
• Solid understanding of deep learning architectures, tokenization, attention mechanisms, and fine-tuning approaches.
• Experience building rapid prototypes and APIs using FastAPI, Flask, or Streamlit.
• Proficiency with Git, REST APIs, and basic system design.

Preferred Skills

• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, or similar).
• Experience with vector databases and embedding workflows (FAISS, Pinecone, Weaviate, Chroma, or Azure AI Search).
• Experience deploying and scaling ML/LLM workloads on cloud platforms (Azure preferred; GCP/AWS acceptable).

Key Skills

Education

Any Graduate