MLOPS Engineer

Design, build, and manage end-to-end automated machine learning pipelines encompassing training and deployment phases.
Orchestrate intricate workflow pipelines systematically utilizing automation frameworks like Apache Airflow.
Implement robust containerized production structures for AI infrastructure utilizing Docker and Kubernetes.
Integrate state-of-the-art Large Language Model workflows into the operating stack using Langchain technologies.
Manage cloud-based machine learning lifecycles seamlessly through the AWS SageMaker platform.
Configure and optimize advanced vector databases to handle semantic search capabilities efficiently.
Monitor live production model performance regularly to maintain high operational throughput and reliability.

Bachelor’s or Master's degree in Computer Science, Data Science, Software Engineering, or a related quantitative technical stream.
3-5 years of professional software engineering experience, highlighting a deep technical mastery over machine learning infrastructure.
Direct hands-on expertise building enterprise MLOps architectures, container systems, and data flow pipelines.
Thorough conceptual and practical clarity across cloud environments, vector databases, and LLM development frameworks.
Strong collaborative, analytical, and technical system troubleshooting capabilities to manage high-availability workflows.