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 environments.
• Develop 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.
• 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.

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.