ML engineer

Description

Today’s world is crime-riddled. Criminals are everywhere, invisible, virtual, and sophisticated. Traditional ways to prevent and investigate crime and terror are no longer enough… 

Technology is changing incredibly fast. The criminals know it, and they are taking advantage. We know it too.  

For nearly 30 years, the incredible minds at Cognyte around the world have worked closely together and put their expertise to work, to keep up with constantly evolving technological and criminal trends and help make the world a safer place with leading investigative analytics software solutions. 

We are defined by our dedication to doing good and this translates to business success, meaningful work friendships, a can-do attitude, and deep curiosity.

We are looking for a Machine Learning Engineer to join our team of our advanced co-pilot product, powered by Generative AI (Gen AI). This product will revolutionize the way users interact with our software, providing real-time assistance , boosting productivity , find Interesting insights and suggest the best next action.

As a Cognyter you will: 

• Lead, collaborate, and drive research in large language models to advance our product.
• Directly contribute to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results.
• Play a significant role in healthy cross-functional collaboration.
• Working closely with the other development teams, architecture, product to create high quality deliverables.
• Taking an active role in our agile processes, design reviews, brainstorming, and knowledge-sharing sessions.

Requirements

For that mission you’ll need:

• M.Sc. or higher in Computer Science, Mathematics, Engineering, or a related field
• 4+ years of hands on experience with coding machine learning modeling based solutions
• Proven experience with Large Language Models (LLM)
• Demonstrated expertise in developing and deploying machine learning models and algorithms.
• Experience in designing ML pipelines, including model versioning, model training & tuning, model deployment, model testing, and monitoring
• Experience with model optimizations (quantization, pruning, etc.)
• Experience with GenAI engineering, implementation and tools, such as LangChain, RAG, Vector Database, transformers, vLLM.
• Team player, responsible, delivery-oriented, details-oriented