AI​/ML Engineer Local LLM and Custom RAG Systems

Key Responsibilities

• Design, develop, and fine-tune local Large Language Models (LLMs) using frameworks like Tensor Flow and PyTorch to ensure high performance and accuracy in real-world applications.
• Build and optimize custom Retrieval-Augmented Generation (RAG) systems by integrating LLMs with enterprise data sources through robust ETL processes and database design.
• Implement unsupervised learning techniques to enhance model capabilities, leveraging big data tools like Hadoop, Spark, and Talend for scalable data processing.
• Deploy AI models efficiently across cloud platforms such as AWS, ensuring scalability, security, and reliability throughout the model lifecycle.
• Develop natural language processing solutions to improve language understanding and generation using machine learning frameworks and NLP techniques.
• Collaborate with cross-functional teams to translate business needs into technical solutions and ensure seamless integration with existing systems.

• Proficiency in machine learning frameworks such as Tensor Flow, PyTorch, or similar tools for model training and deployment.
• Strong knowledge of natural language processing (NLP), including transformer architectures and language modeling techniques.
• Experience with big data technologies including Hadoop, Spark, Kafka, and ETL tools like Talend for data ingestion and processing.
• Familiarity with cloud computing platforms like AWS for deploying scalable AI solutions.
• Programming expertise in Python, Java, C, Bash (Unix shell), and VBA for automation and system integration tasks.