Leading AI Developer

17 hours ago

Palakkad, Kerala, India beBeeArtificialintelligence Full time ₹ 2,00,00,000 - ₹ 2,50,00,000
Senior AI Engineer Job Summary

We are seeking a seasoned AI expert to spearhead the development and optimization of cutting-edge AI-driven applications. As a Senior AI Engineer, you will be responsible for designing, developing, and refining AI solutions leveraging Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) techniques.

The ideal candidate will have a strong understanding of NLP, deep learning, and transformer architectures, as well as proficiency in Python and ML libraries such as TensorFlow and PyTorch. Experience with cloud platforms and MLOps workflows is also highly desirable.

Responsibilities:
  • AI Solution Design and Development: Design, develop, and refine AI solutions using LLMs and RAG techniques to enhance response relevance, contextual accuracy, and knowledge grounding.
  • Pipeline Creation: Create pipelines for data retrieval, indexing, and augmentation to bolster AI performance.
  • Model Implementation and Fine-Tuning: Implement and fine-tune models to improve their performance and scalability.
  • Integration and Collaboration: Integrate AI models with enterprise applications and APIs, and collaborate with vector databases to improve retrieval capabilities.
  • Ethical AI Implementation: Implement AI models that align with business objectives, addressing bias, explainability, and data security.
Requirements:
  • 8+ Years of Experience: Minimum 8 years of experience in software development according to industry standards.
  • Expertise in LLMs and RAG Techniques: Profound expertise in training and deploying LLMs using frameworks like Hugging Face Transformers or OpenAI API, as well as proficiency in RAG techniques and vector search methodologies.
  • Vector Databases: Hands-on experience with vector databases such as FAISS, Pinecone, ChromaDB, or Weaviate.
  • NLP and Deep Learning: Strong understanding of NLP, deep learning, and transformer architectures.
  • Python and ML Libraries: Proficiency in Python and ML libraries such as TensorFlow, PyTorch, etc.
  • Cloud Platforms and MLOps Workflows: Experience with cloud platforms such as AWS, GCP, Azure, and MLOps workflows.
  • Containerization: Familiarity with containerization using Docker and Kubernetes for scalable AI deployments.
  • Problem-Solving and Communication Skills: Strong problem-solving and debugging skills, as well as excellent communication and teamwork abilities.
  • Education: Bachelor's or Master's degree in computer science, AI, Machine Learning, or a related field.

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