Senior Software Engineer

Responsibilities:
Deploy, monitor, and manage ML/AI models on Vertex AI, including training pipelines, model endpoints, and batch prediction workflows.
Provide MLOps support, including troubleshooting model performance, debugging ML pipelines, and resolving production incidents.
Implement AIOps practices, including monitoring, alerting, and automated remediation for AI/ML systems using GCP tools.
Collaborate with data scientists to operationalize ML models, optimize performance, and ensure reliability.
Manage CI/CD pipelines for ML workflows using tools like Cloud Build, GitHub Actions, or Jenkins.
Monitor and optimize GCP resources such as Big Query, Cloud Storage, Dataflow, Dataproc, Cloud Functions, Cloud Run, and Pub/Sub for AI/ML workloads.
Ensure compliance with security, governance, and data privacy best practices in ML workflows.
Build dashboards and observability solutions using Google Cloud Monitoring/Logging, Dataplex, and third-party tools.
Troubleshoot and optimize ML pipelines, including data preprocessing, feature engineering, and model retraining.
Requirements:
2-4 years of experience in MLOps, AI operations, or ML engineering on cloud platforms, preferably GCP.
Hands-on experience with Vertex AI for model deployment, training pipelines, and endpoint management.
2+ years of experience of code management and reliability using GitHub, pytest, and SonarQube.
1+ years of experience with Tableau.
Strong knowledge of MLOps practices, including CI/CD, versioning, automated testing, and model monitoring.
Proficiency in Python, SQL, and ML frameworks like TensorFlow or PyTorch.
Experience with GCP services: BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Functions, Cloud Run, Pub/Sub, Dataplex.
Familiarity with AI monitoring, alerting, and observability tools (Cloud Monitoring, Logging, and Dataplex).
Strong problem-solving skills and ability to troubleshoot ML pipelines and AI system issues.
Understanding of AIOps concepts, including automated anomaly detection, model drift detection, and incident response for AI systems.
Preferred Qualifications:
GCP certifications such as Professional Machine Learning Engineer or Professional Data Engineer.
Experience with feature stores, model registries, or MLflow.
Familiarity with Kubeflow, Airflow, or orchestration tools for ML pipelines.
Experience in data privacy, governance, and compliance for ML systems.

Experience
4-8 yrs