Principal Engineer

Role overview

Principal Engineer at Elektrobit Automotive

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.

Responsibilities

• Drive technology evaluation and adoption of emerging AI platform capabilities.
• Drive AIOps initiatives including intelligent monitoring, anomaly detection, and operational automation.
• Collaborate with architects, AI engineers, data scientists, and business stakeholders.

Employment details

• Location: Bengaluru [India]

Additional role details

• 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.
• Ensure scalability, reliability, security, and cost optimization of AI infrastructure.
• MLOps & AIOps Enablement Lead the implementation of enterprise MLOps frameworks and automation pipelines.
• Enable efficient model training, deployment, monitoring, and lifecycle management.
• Promote self-service AI capabilities for engineering and data science teams.
• Governance & Collaboration Define AI governance, security, compliance, and operational best practices.
• 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.
• Sravani, Konanki extern.Konanki.Sravani@elektrobit.com