Data Scientist

Description

Job Summary: We are seeking an MLOps Engineer to design, implement, and maintain scalable infrastructures for the machine learning model lifecycle and LLM-based systems. Key Responsibilities: 1. Design, implement, and maintain scalable infrastructures for ML and LLMs. 2. Automate ML workflows and manage cloud infrastructure. 3. Collaborate with Data Scientists and Engineers to operationalize models. ESSENTIAL REQUIREMENTS: We are seeking an MLOps Engineer to design, implement, and maintain scalable infrastructures for the machine learning model lifecycle and LLM-based systems. Responsibilities Design training, validation, and deployment pipelines for models. Automate ML workflows (CI/CD, testing, versioning). Implement monitoring and observability systems for models and agents. Manage cloud infrastructure for ML. Ensure scalability, availability, and efficiency of systems. Collaborate with Data Scientists and Engineers to operationalize models. Requirements 2–3 years of experience in MLOps, DevOps, or similar roles. Proficiency in Python and automation tools. Knowledge of containerization (Docker) and orchestration (Kubernetes). Experience with CI/CD (GitHub Actions, GitLab CI, Jenkins). Experience with monitoring tools (Prometheus, Grafana, etc.). DESIRABLE REQUIREMENTS: Cloud experience: AWS (EKS, SageMaker, CloudWatch) or Azure (AKS, Azure ML, Monitor). Experience with ML pipelines (MLflow, Kubeflow, Airflow). Knowledge of LLM-based systems and agents. Agent evaluation and observability (prompt tracking, tracing, debugging). Data management and feature stores. REQUIRED QUALIFICATION: University degree in Computer Engineering, Telecommunications, Mathematics, or other related STEM fields. YEARS OF EXPERIENCE IN THE REQUESTED ROLE: 2–3 years of experience in a similar position.