Machine Learning Engineer
RIVAGO INFOTECH INC.
Concord, United States of America
1 month ago
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
IntermediateJob location
Concord, United States of America
Tech stack
Java
API
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Azure
Continuous Integration
DevOps
Python
Machine Learning
Software Tools
TensorFlow
Software Engineering
SQL Databases
Management of Software Versions
Google Cloud Platform
Cloud Platform System
PyTorch
Spark
Containerization
Scikit Learn
Kubernetes
Information Technology
XGBoost
low-code
Machine Learning Operations
Artificial Intelligence Markup Language (AIML)
Docker
Job description
- Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
- Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).
- Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
- Monitor model performance using observability tools and ensure compliance with
- model governance frameworks (MRM, documentation, explainability)
- Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
- Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no code model development, documentation automation, and rapid deployment
Requirements
- 10+ Years of professional experience in Software Engineering & 3+ Years in AIML,
- Machine Learning Model Operations.
- Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
- Experience with cloud platforms and containerization (Docker, Kubernetes).
- Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
- Solid understanding of software engineering principles and DevOps practices.
- Ability to communicate complex technical concepts to non-technical stakeholders.
