
Data-Driven Solutions Architect
3 days ago
AI/ML Engineer
We are seeking a skilled and proactive professional to join our AI/ML team. The ideal candidate will be responsible for designing, implementing, and maintaining robust MLOps pipelines and infrastructure across cloud platforms.
Key Responsibilities:
- Design and implement model deployment, monitoring, and retraining pipelines.
- Build and maintain CI/CD/CT pipelines for ML workflows using tools like MLFlow, Kubeflow, Airflow, GitHub Actions, and AWS CodePipeline.
- Develop and manage inference, monitoring, and drift detection pipelines (data drift, model drift).
- Architect scalable and secure MLOps infrastructure using Kubernetes, AKS, and Terraform.
- Publish and manage REST APIs for model inference using FastAPI.
- Track experiments and model performance metrics.
- Collaborate with cross-functional teams to raise MLOps maturity across the organization.
- Conduct internal training and presentations on MLOps tools and best practices.
Required Skills & Qualifications:
- Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI
- Big Data & Processing: PySpark, Azure Databricks
- MLOps Tools: MLFlow, Kubeflow, Airflow, GitHub Actions, AWS CodePipeline
- Infrastructure & Automation: Kubernetes, AKS, Terraform, FastAPI
- Programming Languages: Python (ML and automation), Bash, Unix CLI
- Bachelor's or Master's degree in Computer Science, Data Science, or related field.
- Proven experience in operationalizing ML models using MLOps frameworks.
- Strong understanding of ML/AI concepts and hands-on experience in model development.
- Experience with cloud-native development and container orchestration.
- Familiarity with agile methodologies and DevOps practices.
Preferred Qualifications:
- Certification in AWS, Azure, or GCP.
- Experience with DataRobot, DKube, or similar platforms.
- Exposure to enterprise-level ML systems and governance.
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