About the role:

We’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.

Responsibilities:

• Build 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 skills:

• 2-4 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 have:

• Experience 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 get:

• Work 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 benefits:

• Professional 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.