Senior AI/ML Engineer
About the Company
OLSYS Ltd provides full-service solutions for mid-market and enterprise organizations.
As an enterprise software development company, we are building long-term partnerships helping our clients accelerate their digital experiences with reasonable IT investments. Our tailored approach, e-commerce focus, and flexible solutions allow us to design, develop, and deliver scalable, integrated commerce platforms that drive profits and boost the business.
15+ years of experience, 100+ projects, 50+ specialists
We are seeking a highly skilled and motivated Senior AI/ML Engineer to join our dynamic team and take part in creating a new product.
Requirements:
Experience: Demonstrated experience of at least 5 years in developing AI/ML solutions with a focus on LLM and LLM frameworks (e.g. LangChain). Strong knowledge and practical experience in Generative AI, Interactive Chatbots, Graph databases.
•Data Processing: Experience in preprocessing and handling large-scale, unstructured text data, and the ability to perform data analysis and validation.
•Problem-solving and Innovation: Proven ability to tackle complex technical challenges, think innovatively, and propose creative solutions to real-world problems.
•Communication: Excellent verbal and written communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders.
English level: Upper-Intermediate
Responsibilities:
● Interact with the modern LLMs and adopt the best practices of their usage.
● R&D activities (experimenting with LLMs): Generate code based on the input description. Build test cases based on the user scenarios.
● Performance Monitoring: Monitor the performance of the models and make adjustments as necessary to maintain or enhance model accuracy and efficiency.
● Data Preprocessing and Annotation: Work with the Data Engineering team top reprocess and annotate the training data for AI/ML models. Collaborate with subject matter experts to create labeled datasets that facilitate supervised and unsupervised learning.
● Prototyping and Evaluation: Rapidly prototype and iterate different AI/ML approaches to identify the most suitable solution to meet the requirements. Perform rigorous evaluations to measure the model's performance against defined metrics.
● Documentation: Document all stages of the AI/ML development process, including design choices, architecture, algorithms, and evaluation results, ensuring knowledge transfer and team understanding.
● Collaboration: Collaborate closely with cross-functional teams, including Data Scientists and Software Engineers.
● Stay Abreast of Industry Trends: Keep up-to-date with the latest advancements in AI/ML, NLP, LLM and other relevant technologies. Integrate new research findings and methodologies into the project as applicable.
● Data Processing: Experience in preprocessing and handling large-scale, unstructured text data, and the ability to perform data analysis and validation.
● Problem-solving and Innovation: Proven ability to tackle complex technical challenges, think innovatively, and propose creative solutions to real-world problems.
● Communication: Excellent verbal and written communication skills to effectively convey complex technical concepts to both technical and non-technical stakeholders.
English level: Upper-Intermediate
Responsibilities:
● Interact with the modern LLMs and adopt the best practices of their usage.
● R&D activities (experimenting with LLMs): Generate code based on the input description. Build test cases based on the user scenarios.
● Performance Monitoring: Monitor the performance of the models and make adjustments as necessary to maintain or enhance model accuracy and efficiency.
● Data Preprocessing and Annotation: Work with the Data Engineering team top reprocess and annotate the training data for AI/ML models. Collaborate with subject matter experts to create labeled datasets that facilitate supervised and unsupervised learning.
● Prototyping and Evaluation: Rapidly prototype and iterate different AI/ML approaches to identify the most suitable solution to meet the requirements. Perform rigorous evaluations to measure the model's performance against defined metrics.
● Documentation: Document all stages of the AI/ML development process, including design choices, architecture, algorithms, and evaluation results, ensuring knowledge transfer and team understanding.
● Collaboration: Collaborate closely with cross-functional teams, including Data Scientists and Software Engineers.
● Stay Abreast of Industry Trends: Keep up-to-date with the latest advancements in AI/ML, NLP, LLM and other relevant technologies. Integrate new research findings and methodologies into the project as applicable.
