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AI Engineer

Responsibilities:

  • Design, develop, and optimize AI models and algorithms to address business challenges

  • Conduct LLM fine-tuning, embedding, and implementation of Retrieval-Augmented Generation (RAG)

  • Collaborate with data scientists and engineers to deploy AI models in production environmentsDevelop machine learning models, including supervised and unsupervised learning, deep learning, and natural language processing (NLP) applications

  • Understand the infrastructure requirements for running AI solutions in production

  • Utilize and demonstrate expertise with popular LLM models such as DeepSeek, OpenAI GPT, LLaMA, and others

  • Process large-scale data to train models and evaluate their performance

  • Research and implement the latest AI technologies to enhance products and services

  • Ensure the scalability, efficiency, and reliability of AI systems in production

  • Integrate AI models with existing systems and processes to deliver comprehensive solutions

  • Monitor model performance and make adjustments for continuous improvement

  • Write and maintain technical documentation for AI solutions.


Qualifications:

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field

  • Experience using AI frameworks and libraries such as TensorFlow, PyTorch, or Keras

  • Strong understanding of machine learning algorithms, neural networks, and deep learning

  • Experience in LLM fine-tuning, embedding, and RAG implementation, as well as Vector Indexing and Vector DBs (e.g., LLamaIndex/FAISS, Chroma)

  • Familiarity with popular LLM models such as OpenAI, Claude, LLaMA, and similar

  • Understanding of the infrastructure required to run AI solutions in production

  • Proficiency in programming languages such as Python, and Javascript

  • Familiarity with data processing and analysis tools such as Pandas, NumPy, and scikit-learn

  • Experience with cloud platforms (e.g., AWS, Google Cloud, Microsoft Azure) and containerization (e.g., Docker, Kubernetes)

  • Strong problem-solving skills and the ability to work in fast-paced environments

  • Excellent communication and collaboration skills

  • Experience using AI frameworks and libraries such as TensorFlow, PyTorch, or Keras is preferred

 

Preferred Skills:

  • Experience with natural language processing (NLP) and computer vision

  • Understanding of AI ethics, data privacy, and bias mitigation techniques

  • Experience with MLOps practices for managing the machine learning lifecycle

  • Knowledge of big data technologies (e.g., Hadoop, Spark) is a plus

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