LLM Engineer - AI & Data solutions (MNC)

The ideal candidate should have proven experience in end to end process from design, develop, deploy to product support of data science & analytics projects.

Key Responsibilities

• LLM Development & Optimization:
  Design, fine-tune, and optimize large language models for various business use cases including virtual assistants, summarization tools, content generation, and knowledge extraction.

• Model Evaluation & Benchmarking:
  Evaluate model performance using appropriate metrics and implement strategies for continuous improvement across multiple languages and domains.

• Infrastructure & Deployment:
  Collaborate with MLOps and cloud engineering teams to deploy LLMs at scale, ensuring low latency, high availability, and cost efficiency.

• Prompt Engineering & Retrieval-Augmented Generation (RAG):
  Develop advanced prompt engineering strategies and integrate LLMs with external knowledge bases using techniques like RAG.

• Research & Innovation:
  Stay updated with the latest advancements in LLMs (e.g., GPT, LLaMA, Claude, Mistral), and contribute to internal research initiatives and AI strategy.

• Security & Compliance:
  Ensure responsible AI practices, including data privacy, model explainability, fairness, and alignment with global regulatory standards.

\Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or related field (PhD is a plus)
• 3+ years of experience in AI/ML, with at least 1–2 years focused on large language models or NLP
• Hands-on experience with frameworks such as Hugging Face Transformers, LangChain, OpenAI API, or LLamaIndex
• Proficient in Python, and familiar with libraries like PyTorch or TensorFlow
• Experience with LLM fine-tuning, LoRA, PEFT, or quantization techniques
• Strong understanding of transformer architectures and attention mechanisms
• Experience deploying models on cloud platforms (AWS, Azure, GCP) using containerization and CI/CD pipelines