onsite
MLOps Engineer - AgileEngine
MLOps Engineer
Mid‑ to senior‑level MLOps Engineer responsible for building and maintaining scalable ML pipelines, automating model deployment, and ensuring reliable infrastructure on cloud platforms.
About the role
Key Responsibilities
- Design, implement, and maintain end‑to‑end machine‑learning pipelines from data ingestion to model serving.
- Develop and manage containerized environments using Docker and orchestrate workloads with Kubernetes.
- Automate CI/CD workflows for model training, testing, and deployment, integrating tools such as GitHub Actions or Jenkins.
- Monitor, troubleshoot, and optimize model performance and infrastructure costs on AWS.
- Collaborate with data scientists and software engineers to standardize model versioning, reproducibility, and governance using MLflow or similar platforms.
Requirements
- 3+ years of hands‑on experience in MLOps or DevOps roles, with a strong focus on machine‑learning workflows.
- Proficiency in Python for scripting, automation, and building custom tooling.
- Deep knowledge of containerization (Docker) and orchestration (Kubernetes) in production environments.
- Experience implementing CI/CD pipelines and infrastructure‑as‑code on AWS (EC2, S3, EKS, SageMaker, etc.).
- Familiarity with model tracking and lifecycle management tools such as MLflow, DVC, or similar.
Skills
pythondockerkubernetescicdawsmlflow