MLOps Engineer

Описание

About The RoleWe’re seeking an MLOps Engineer to join AI projects involving machine learning infrastructure, pipeline development, and modern cloud-native tooling. You’ll work with AWS SageMaker, Bedrock, and related services to automate model training, deployment, and monitoring — while optionally supporting data engineering workflows and cloud infrastructure design.Depending on the project, your tasks will range from building robust ML pipelines to supporting LLM-based analytics agents with real-time data.ResponsibilitiesBuild and maintain MLOps pipelines using Amazon SageMaker (training, evaluation, inference);Integrate with CI/CD systems (e.g., CodePipeline, CodeBuild) to deploy models and pipelines;Configure monitoring for data/model drift using SageMaker Model Monitor and CloudWatch;Collaborate with data scientists and engineers on training, tuning, and deployment workflows;Apply ML experiment tracking (e.g., SageMaker Experiments or MLflow);Assist with guardrails and quality checks in LLM-integrated applications;Document processes, troubleshoot issues, and participate in sprint planning sessions.Required Skills2-4 years of experience in MLOps, ML engineering, or cloud-based ML deployment;Hands-on experience with AWS SageMaker and core MLOps tools;Strong Python skills, especially around ML and automation;Familiarity with CI/CD practices in cloud environments;Understanding of model lifecycle management (training → deployment → monitoring).Nice To HaveExperience with data pipeline tools: Amazon EMR, Glue, Athena, Spark;Exposure to LLM services (e.g., AWS Bedrock) and vector search solutions;Knowledge of Airflow, feature stores, and API-based agent interfaces;Infrastructure-as-Code experience (e.g., CloudFormation, Terraform);Familiarity with Salesforce or similar data source integrations.What You’ll GetWork across innovative AI/ML use cases (predictive analytics, conversational agents);Access to a skilled team with clear project requirements and agile planning;Opportunity to grow toward senior MLOps, data engineering, or platform roles;Flexible work setup, real-world deployment experience, and mentorship if needed.Our BenefitsProfessional and career growth promotion;Competitive salary;Paid vacations and sick leaves;Internal Medical Program;Program for veterans (which includes mentorship, an accessible office for individuals with disabilities, legal support, and additional benefits);Flexible working hours;Regular corporate social activities;Regular technical training at our office;English courses;Gym, etc.