Overview:

We are seeking a Principal Engineer – AI Platforms, MLOps & AIOps with 15+ years of experience in Software Engineering, Cloud, DevOps, and AI Platform Engineering. This role will define the technical strategy, architecture, and governance of enterprise AI platforms while driving scalable AI adoption and operational excellence across the organization.

Responsibilities:

Technical Leadership & Architecture

- Define the roadmap and architecture for enterprise AI platforms, MLOps, and AIOps.
- Establish standards for AI model development, deployment, monitoring, and governance.
- Drive technology evaluation and adoption of emerging AI platform capabilities.
- Ensure scalability, reliability, security, and cost optimization of AI infrastructure.

MLOps & AIOps Enablement

- Lead the implementation of enterprise MLOps frameworks and automation pipelines.
- Enable productive model training, deployment, monitoring, and lifecycle management.
- Drive AIOps initiatives including intelligent monitoring, anomaly detection, and operational automation.
- Promote self-service AI capabilities for engineering and data science teams.

Governance & Collaboration

- Define AI governance, security, compliance, and operational best practices.




- Collaborate with architects, AI engineers, data scientists, and business stakeholders.
- Mentor senior engineers and provide technical guidance across teams.

Qualifications:

Experience

- 15+ years of experience in Software Engineering, Cloud, DevOps, or Platform Engineering.
- 5+ years leading MLOps, AIOps, or AI platform initiatives.
- Proven experience architecting and operating large-scale cloud-native platforms.

Technical Skills

- MLOps platforms: MLflow, Kubeflow, Azure ML, SageMaker, Vertex AI, or similar.
- Cloud platforms: Azure, AWS, or GCP.
- Docker, Kubernetes, CI/CD, Infrastructure as Code (Terraform, Ansible).
- Monitoring and observability tools such as Grafana, Prometheus, ELK, Datadog, or Splunk.
- Python and automation development.
- Strong understanding of AI governance, security, and model lifecycle management.

Success Measures

- Successful enterprise adoption of AI platforms and MLOps capabilities.
- Improved deployment speed, platform reliability, and operational efficiency.
- Scalable, secure, and governed AI infrastructure.
- Delivery of strategic AI initiatives aligned with business objectives.

📌 Principal Engineer (India)
🏢 Elektrobit Automotive
📍 India