Hands‑on experience with machine learning frameworks such as PyTorch, TensorFlow, or Keras. Familiarity with Large Language Models (LLMs), prompt engineering, and fine‑tuning techniques (LoRA, QLoRA). Exposure to RAG (Retrieval‑Augmented Generation) and hybrid search methodologies. Understanding of AI model deployment, MLOps practices, and containerization using Docker. Strong proficiency in Python for AI/ML development, scripting, and data preprocessing. Experience with Generative AI libraries and tools such as LangChain, Hugging Face, or LlamaIndex (preferred). Familiarity with Git and version control best practices. Awareness of AI governance, Responsible AI, and data privacy principles.
Implement, fine‑tune, and optimize Generative AI models in collaboration with senior team members. Contribute to prompt engineering, LLM-based feature development, and AI‑powered automation workflows. Assist in deployment, monitoring, and maintenance of AI/ML models in production environments. Work with data scientists and engineering teams to ensure smooth integration of AI capabilities. Perform data preprocessing, feature engineering, and support API development for AI applications. Participate in code reviews, testing, documentation, and quality assurance activities. Stay current with the latest advancements in Generative AI, LLMs, RAG architectures, and share insights with the team.
Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related discipline. 5+ years of experience in AI/ML development, including exposure to Generative AI projects. Proven experience delivering AI/ML solutions in collaborative, agile environments. Exposure to cloud‑based AI/ML platforms such as AWS, GCP, or Azure is an added advantage.
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