Senior DevOps Specialist

We are seeking a Senior DevOps Specialist to architect, implement, and optimize AI-driven operational frameworks across AIOps, MLOps, and DataOps.

The ideal candidate will design and enhance intelligent automation, predictive analytics, and resilient pipelines for large-scale data engineering, AI/ML deployments, and IT operations. This role requires deep expertise in AI/ML automation, data-driven DevOps strategies, observability frameworks, and cloud-native orchestration.

The core responsibilities for the job include the following :

Design and Architecture AIOps : AI-Driven IT Operations and Automation :

- Architect AI-powered observability platforms, ensuring predictive incident detection and autonomous IT operations.

- Implement AI-driven root cause analysis (RCA) for proactive issue resolution and performance optimization.

- Design self-healing infrastructures leveraging machine learning models for anomaly detection and remediation workflows.

- Establish event-driven automation strategies, enabling autonomous infrastructure scaling and resilience engineering.

MLOps : Machine Learning Lifecycle Optimization :

- Architect end-to-end MLOps pipelines, ensuring automated model training, validation, deployment, and monitoring.

- Design CI/CD pipelines for ML models, embedding drift detection, continuous optimization, and model explainability. Implement feature engineering pipelines, leveraging data versioning, reproducibility, and intelligent retraining techniques.

- Ensure secure and scalable AI/ML environments, optimizing GPU-accelerated processing and cloud-native model serving.

DataOps : Scalable Data Engineering and Pipelines :

- Architect data processing frameworks, ensuring high-performance, real-time ingestion, transformation, and analytics.

- Build data observability platforms, enabling automated anomaly detection, data lineage tracking, and schema evolution.

- Design self-optimizing ETL pipelines, leveraging AI-driven workflows for data enrichment and transformation.

- Implement governance frameworks, ensuring data quality, security, and compliance with enterprise standards.

Automation and API Integration :

- Develop Python or Go-based automation scripts for AI model orchestration, data pipeline optimization, and IT workflows.

- Architect event-driven xOps frameworks, enabling intelligent orchestration for real-time workload management.

- Implement AI-powered recommendations, optimizing resource allocation, cost efficiency, and performance benchmarking.

Cloud-Native and DevOps Integration :

- Embed AI/ML observability principles within DevOps pipelines, ensuring continuous monitoring and retraining cycles.

- Architect cloud-native solutions optimized for Kubernetes, containerized environments, and scalable AI workloads.

- Establish AIOps-driven cloud infrastructure strategies, automating incident response and operational intelligence.

Requirements :

- xOps Expertise.

- Deep expertise in AIOps, MLOps, and DataOps, designing AI-driven operational frameworks.

- Proficiency in automation scripting, leveraging Python, Go, and AI/ML orchestration tools.

- Strong knowledge of AI observability, ensuring resilient IT operations and predictive analytics.

- Extensive experience in cloud-native architectures, Kubernetes orchestration, and serverless AI workloads.

- Ability to troubleshoot complex AI/ML pipelines, ensuring optimal model performance and data integrity.

Preferred Certifications (Optional) :

- AWS Certified Machine Learning Specialist.

- Google Cloud Professional Data Engineer.

- Kubernetes Certified Administrator (CKA).

- DevOps Automation and AIOps Certification.