Straive
Website:
straive.com
Job details:
We are looking for a highly skilled MLOps Engineer to build, deploy, automate, and manage Machine Learning solutions in production. The ideal candidate should have hands-on experience in designing scalable ML pipelines, deploying models, automating workflows, monitoring model performance, and implementing CI/CD for machine learning applications. The role requires close collaboration with Data Scientists, Data Engineers, and DevOps teams to ensure reliable and efficient ML model lifecycle management.
Key Responsibilities
- Design, develop, and maintain end-to-end MLOps pipelines for training, testing, deployment, and monitoring of machine learning models.
- Deploy machine learning models to cloud and on-premise environments using containerization and orchestration technologies.
- Build and automate CI/CD pipelines for machine learning applications.
- Develop reusable ML workflows for model training, validation, deployment, and versioning.
- Implement model monitoring, performance tracking, drift detection, and automated retraining strategies.
- Manage ML artifacts, datasets, feature stores, and model registries.
- Collaborate with Data Scientists to operationalize machine learning models.
- Optimize infrastructure for scalable and cost-effective model deployment.
- Troubleshoot production issues and ensure high availability of ML services.
- Maintain security, governance, and compliance standards across ML platforms.
Required Skills
- 5–8 years of IT experience with at least 3+ years of hands-on experience in MLOps.
- Strong programming experience in Python.
- Hands-on experience with one or more MLOps platforms:
- MLflow
- Kubeflow
- Azure Machine Learning
- AWS SageMaker
- Google Vertex AI
- Experience deploying machine learning models using:
- Docker
- Kubernetes
- Strong knowledge of CI/CD tools:
- Jenkins
- Azure DevOps
- GitHub Actions
- GitLab CI/CD
- Experience with version control using Git.
- Strong understanding of machine learning lifecycle and model management.
- Experience with REST APIs for model serving.
- Knowledge of SQL and data engineering concepts.
- Experience with Linux and shell scripting.
Cloud Platforms
Experience with at least one of the following:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
Good to Have
- Experience with Apache Airflow or Prefect for workflow orchestration.
- Knowledge of Terraform or Infrastructure as Code (IaC).
- Experience with Spark or PySpark for large-scale data processing.
- Familiarity with Databricks.
- Experience with Kafka or other streaming platforms.
- Knowledge of Feature Store implementation.
- Experience with monitoring tools such as Prometheus, Grafana, ELK, or Azure Monitor.
- Exposure to LLMOps, Generative AI, or Large Language Models is an added advantage.
Click on Apply to know more.
