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Artificial Intelligence Engineer - RAG / LangChain

Artificial Intelligence Engineer - RAG / LangChain

Risosu ConsultingMumbai
30+ days ago
Job description

Job Description :

We are seeking a highly skilled and motivated AI Engineer with deep expertise in Generative AI, Large Language Models (LLMs), and Transformer-based architectures. The ideal candidate will have hands-on experience in building, fine-tuning, and deploying cutting-edge AI / ML models with a strong grasp of real-world applications.

Key Responsibilities :

  • Design, develop, and deploy AI / ML models with a focus on Generative AI and LLMs.
  • Fine-tune transformer-based models using Hugging Face, LangChain, or similar frameworks.
  • Implement end-to-end pipelines for training, evaluation, and serving of models.
  • Collaborate cross-functionally with product, engineering, and data teams to integrate AI capabilities into applications.
  • Conduct exploratory data analysis and build predictive analytics models.
  • Optimize and scale models for performance, cost, and latency.
  • Contribute to research and stay current with the latest advancements in deep learning and MML (Multimodal Machine Learning).

Key Skills and Requirements :

  • Strong hands-on experience with Generative AI, LLMs (e.g., GPT, LLaMA, Mistral), and Transformer models.
  • Proficient in Python, SQL, and deep learning frameworks such as PyTorch or TensorFlow.
  • Experience with LangChain, LangGraph, and Hugging Face Transformers.
  • Ability to fine-tune and evaluate foundation models on custom datasets.
  • Familiarity with MML (Multimodal Learning) including text, image, and audio integration.
  • Strong understanding of predictive analytics and statistical modeling.
  • Experience with model deployment and serving in production environments is a plus.
  • Excellent problem-solving and communication skills.
  • Preferred Qualifications :

  • Bachelors / Masters degree in Computer Science, AI / ML, Data Science, or a related field.
  • Experience working with vector databases, embedding models, or RAG architectures.
  • Familiarity with cloud platforms (AWS, Azure, or GCP) for scalable model training / deployment
  • (ref : hirist.tech)

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