AI- Engineer

Requirements

About the Role:

AI Engineer with a focus on Large Language Models (LLMs)

Key Responsibilities:

• Design, build, and optimize AI models with a strong focus on Large Language Models (e.g., GPT, BERT, T5, LLaMA).
• Develop and implement RAG (Retrieval-Augmented Generation) pipelines and vector search solutions to enhance model accuracy and contextual understanding.
• Fine-tune and evaluate pre-trained LLMs on domain-specific datasets to meet performance and accuracy benchmarks.
• Build robust APIs and tools for integrating AI-powered solutions into production systems and customer-facing applications.
• Collaborate with data scientists, product managers, and software engineers to align AI functionality with business needs.
• Conduct research and stay up-to-date with the latest developments in LLMs, NLP, and generative AI.
• Apply techniques such as prompt engineering, few-shot learning, and instruction tuning to improve model interaction quality.
• Ensure data integrity, performance optimization, and model explainability in all deployed solutions.
• Participate in code reviews, documentation, and knowledge sharing to maintain engineering best practices.
  

Requirements:

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Minimum 3 years of hands-on experience working with AI/ML models, with at least 1-2 years focused on LLMs and NLP applications.
• Proficiency in Python and popular AI frameworks/libraries such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, or OpenAI APIs.
• Experience with vector databases like FAISS, Pinecone, or Weaviate for retrieval tasks.
• Strong knowledge of LLM fine-tuning, embeddings, tokenization, and transformer-based architectures.
• Solid understanding of data preprocessing, text generation, and evaluation metrics in NLP tasks.
• Familiarity with deployment tools and cloud platforms such as AWS, Azure, or GCP.
• Ability to work in a collaborative, agile environment and contribute to cross-functional AI initiatives.
  

Preferred Skills:

• Experience working with RAG pipelines, multi-turn conversations, or enterprise search.
• Background in knowledge graphs, ontologies, or semantic search.
• Exposure to MLOps practices and automated model deployment pipelines.
• Publications or contributions in the field of AI/NLP are a plus.