Sr. Machine Learning Engineer - AI

Immigration sponsorship is not available for this position What you can expect: We are seeking a highly skilled and motivated Machine Learning Engineer to join our team and play a key role in developing and maintaining our knowledge graph service. As a Machine Learning Engineer specializing in knowledge graphs, you will work closely with cross-functional teams to design, implement, and optimize algorithms and models that enable efficient representation, integration, and retrieval of structured and unstructured data.

Architect and optimize large-scale Machine Learning workflows to process structured and unstructured data for knowledge graphs and search services; Architect and optimize large-scale Machine Learning workflows to process structured and unstructured data for knowledge graphs and search services; Deploy, monitor, and maintain machine learning models (LLMs and agentic workflows) in a microservice environment using Docker, Kubernetes (AWS EKS), and internal platforms (ZCP); Collaborate with cross-functional teams of engineers, product managers, and domain experts to define requirements and implement scalable, production-ready Machine Learning services. Maintain comprehensive documentation for all AI search systems, workflows, and services using Confluence, Zoom Docs, and LucidChart; Develop business logic for routing requests to Machine Learning models and managing feature stores; Implement advanced authentication and security mechanisms for Machine Learning pipelines, including asymmetric JWT and secure key management (AWS KMS, internal CSMS. Work with asynchronous, distributed messaging frameworks (AsyncMQ). Optimize knowledge graph algorithms for performance, scalability, and reliability. Conduct research on cutting-edge Machine Learning, NLP, and knowledge graph techniques, evaluating external data sources to enhance search and document understanding capabilities; Develop/maintain documentation, best practices, guidelines; Mentor junior engineers and contribute to team knowledge-sharing, best practices, and internal technical guidelines; Lead the design, development, and deployment of AI search pipelines, including document understanding and retrieval systems, using libraries such as Docling; Evaluate external data sources to enhance AI search/document understanding; Develop performance metrics, monitoring systems, and optimizations for AI search pipelines to ensure scalability, efficiency, and low latency; Develop and maintain AI-driven search and ranking algorithms for enterprise-scale information retrieval and RAG (Retrieval-Augmented Generation) systems; Design and maintain data ingestion, preprocessing, and transformation pipelines for both structured and unstructured data, ensuring high data quality and reliability.

Requires a bachelor’s degree in computer science, Communications Engineering, a related field, or a foreign degree equivalent. Must have 3 years of experience in job offered or related occupation. Must have 3 years of experience in the following: 3 years of experience in design, development, and deployment of document understanding and processing pipelines; 3 years of experience in deploy, monitor, and maintain machine learning models (LLMs and agentic workflows) in a microservice environment using Docker, Kubernetes (AWS EKS), and internal platforms (ZCP); 3 years of experience in evaluating external data sources to enhance AI search/document understanding; 3 years of experience in developing performance metrics, monitoring systems, and optimizations for AI search pipelines to ensure scalability, efficiency, and low latency; 3 years of experience in design and maintaining data ingestion, preprocessing, and transformation pipelines for both structured and unstructured data, ensuring high data quality and reliability; 3 years of experience in architect and optimizing large-scale Machine Learning workflows to process structured and unstructured data