AI Engineer

Xyris Digital Solutions

Collect, clean, and preprocess large volumes of structured and unstructured data to ensure its quality and usability for machine learning tasks. Identify data patterns, correlations, and anomalies to extract meaningful insights.

Utilize domain knowledge and statistical techniques to create informative features.

Design, develop, and implement machine learning models and algorithms to solve complex business problems.

Train and evaluate traditional machine learning and deep learning models using large datasets and ensure scalability and efficiency.

Collaborate with software engineers to integrate models into existing systems or develop APIs for model serving.

Continuously evaluate model performance and fine-tune algorithms to optimize accuracy, precision, recall, and other relevant metrics.

Work closely with business stakeholders to understand requirements, develop solutions, and present findings. Clearly communicate complex technical concepts to both technical and non-technical audiences.

Stay updated with the latest advancements in artificial intelligence, machine learning, and data science. Explore new algorithms, frameworks, and tools to improve the efficiency and effectiveness of the team's work.

Design and develop advanced Generative AI applications, including Retrieval-Augmented Generation (RAG), Agentic AI systems, and prompt engineering techniques to address diverse business use cases such as automated data extraction, document understanding, and natural language-to-SQL generation.

Perform fine-tuning and evaluation of Large Language Models (LLMs) to enhance performance, accuracy, and alignment with domain-specific requirements, leveraging industry-standard datasets and evaluation benchmarks.

Utilize and integrate multiple frameworks and tools (e.g., LangChain, LlamaIndex, Hugging Face, OpenAI, etc.) to build scalable and production-ready generative AI solutions tailored to enterprise needs.