
AI Operations Engineer
2 days ago
GSPANN Technologies, Inc is hiring an AI Operations Engineer. The role focuses on deploying ML models, automating CI/CD pipelines, and implementing AIOps solutions.
Location: Hyderabad / Pune / Gurugram
Role Type: Full Time
Experience: 3+ Years
Responsibilities- Build, automate, and manage continuous integration and continuous deployment (CI/CD) pipelines for machine learning (ML) models.
- Partner with data scientists to transition ML models from experimentation to production environments.
- Use tools such as Docker, Kubernetes, MLflow, or Kubeflow to deploy, monitor, and maintain scalable ML systems.
- Implement systems for model versioning, model drift detection, and performance tracking.
- Maintain reproducibility and traceability of ML experiments and outputs.
- Design and implement AIOps frameworks to enable predictive monitoring, anomaly detection, and intelligent alerting.
- Leverage observability tools, including Prometheus, Grafana, ELK (Elasticsearch, Logstash, Kibana), Dynatrace, and Datadog, to collect and analyze infrastructure and application metrics.
- Utilize machine learning and statistical methods to identify patterns, automate root cause analysis, and forecast system behavior.
- Write and maintain automation scripts that support remediation and incident response.
- Bachelor's or Master's degree in Computer Science, Data Science, Information Technology, or a related discipline.
- Certification in DevOps or MLOps from AWS or GCP is preferred.
- Understand Site Reliability Engineering (SRE) practices and metrics such as Service-Level Agreements (SLAs), Service-Level Indicators (SLIs), and Service-Level Objectives (SLOs).
- Demonstrate strong programming proficiency in Python.
- Work with ML lifecycle platforms such as MLflow, Kubeflow, TensorFlow Extended (TFX), and Data Version Control (DVC).
- Use Docker and Kubernetes for containerization and orchestration.
- Employ CI/CD tools including GitHub Actions, Jenkins, and GitLab CI/CD.
- Operate monitoring and logging systems like Prometheus, Grafana, ELK, Datadog, and Splunk.
- Possess hands-on experience with cloud platforms such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
- Apply DevOps principles effectively and use infrastructure-as-code tools like Terraform and Ansible.
- Handle projects involving Natural Language Processing (NLP), time-series forecasting, or anomaly detection models.
- Build and manage large-scale distributed computing environments.
- Mid-Senior level
- Full-time
- Engineering and Information Technology
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