Senior ML Engineer / MLOps Engineer (Agentic AI & Cloud Platforms)
New
B
BillenniumAI and machine learning
Workplace type: remote; Locations: Warszawa, N/A, Warszawa, Country code: PLFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- En C1
- Required Skills
- AWSPythonKubernetesMachine LearningPyTorchTensorflowMLOps
Requirements
- Strong understanding of machine learning concepts and the end-to-end model lifecycle.
- Hands-on experience with PyTorch or TensorFlow and experience deploying ML models into production.
- Experience building AI-powered applications using modern LLM frameworks.
- Understanding of Agentic AI, AI agents, tool calling, RAG, workflow orchestration, and retrieval-based systems.
- Strong Python development skills and experience building production-grade APIs with FastAPI, Flask, or similar frameworks.
- Solid software engineering and system design principles, including testing, version control, and CI/CD practices.
- Hands-on experience with Kubernetes and Docker.
- Experience with Kubeflow, ML pipeline orchestration, and building and operating MLOps platforms.
- Experience with Infrastructure as Code, such as Terraform, and understanding of monitoring and observability for AI and ML systems.
- Strong AWS experience, including EKS, EC2, S3, and Lambda; Azure or GCP experience is welcomed.
- Understanding of cloud-native architecture, networking, and security principles.
Responsibilities
- Design, develop, and deploy production-grade AI and machine learning solutions.
- Build AI agents and agentic workflows to automate business processes and support decision-making.
- Develop LLM-powered applications and orchestrate multi-step AI workflows.
- Create APIs and microservices that expose ML models and AI capabilities.
- Design and maintain ML training, inference, and agent orchestration pipelines.
- Integrate AI agents with enterprise platforms, data sources, and business applications.
- Monitor, evaluate, and optimize AI and ML systems in production.
- Build and maintain cloud-native MLOps and AI platforms, including Kubernetes-based environments and cloud infrastructure.
- Build and operate CI/CD pipelines and implement Infrastructure as Code using Terraform or similar technologies.
- Ensure platform reliability, security, governance, and observability; troubleshoot production issues and improve performance.
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