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.
