Eduard Mihai

Results-driven Senior DevOps Engineer with 10 years of experience building and operating cloud-native platforms across telecommunications, AI-driven automation, and healthcare technology domains. Experienced in designing reliable infrastructure, CI/CD automation, containerized services, and secure cloud environments using Kubernetes, Terraform, Docker, Python, and modern observability practices. Hands-on experience delivering scalable AI data platforms, distributed backend services, and production systems with strong focus on reliability, security, and operational efficiency across AWS, Azure, and GCP-based environments.

Cloud Engineer / Data Engineer - TerraEye

(2025-12)

• Developed cloud infrastructure supporting an AI-powered satellite data processing platform for mineral exploration workflows, building reliable environments for large-scale geospatial analytics using Kubernetes, Terraform, Docker, and automated deployment patterns.
• Improved inconsistent deployment processes for machine learning services by implementing reusable CI/CD pipelines with GitHub Actions, container image validation, and automated environment provisioning, reducing manual release activities and improving delivery consistency.
• Designed and maintained infrastructure-as-code workflows using Terraform across cloud environments, applying modular configurations, secure credential handling, and repeatable provisioning practices to support scalable AI processing workloads.
• Optimized containerized workloads running data processing services by configuring Kubernetes resources, Helm deployments, and workload scaling strategies, improving application stability during intensive satellite image processing operations.
• Implemented platform observability using cloud monitoring, logging pipelines, OpenTelemetry, and Grafana dashboards to identify infrastructure issues earlier and improve operational visibility across distributed AI services.
• Strengthened cloud security practices by integrating IAM controls, secrets management, vulnerability scanning, and secure network configurations, ensuring protected access patterns for sensitive exploration datasets and production services.
• Supported distributed service architecture by improving API deployment workflows, Linux-based runtime environments, and networking configurations, enabling reliable communication between data ingestion, processing, and AI inference components.
• Worked within Agile engineering workflows by documenting operational procedures, deployment runbooks, and troubleshooting practices while collaborating with software and data teams to improve platform reliability and maintainability.

Data Scientist - Truecaller

(2022-12 - 2025-12)

• Contributed to telecommunications-scale fraud detection and caller identification platforms processing large volumes of communication signals, supporting reliable AI-driven services through scalable cloud infrastructure and production data workflows.
• Built and maintained backend data processing pipelines using Python, Spark, Kafka, and cloud services to support spam detection models, improving data availability and reducing delays in model feature generation workflows.
• Automated machine learning deployment workflows by integrating model validation, containerized execution environments, and CI/CD automation practices, increasing consistency between development, testing, and production environments.
• Developed secure API-driven services for AI model integration using Python-based frameworks, Docker containers, and cloud runtime environments, supporting high-volume prediction requests with reliable service availability.
• Enhanced platform monitoring by integrating logging, metrics, and alerting solutions with cloud monitoring tools and operational dashboards, helping engineering teams identify performance issues across distributed services.
• Applied infrastructure reliability practices including Kubernetes-based deployments, environment configuration management, and automated testing pipelines to support scalable communication intelligence applications used by global users.
• Improved security controls around data processing workflows by applying access management principles, encrypted data handling, and controlled service communication patterns across cloud-based analytics environments.
• Collaborated with software engineers, data specialists, and product teams in Agile delivery cycles to document technical solutions, improve deployment processes, and deliver reliable AI capabilities through iterative releases.

AI Software Engineer - ServiceNow

(2020-01 - 2022-11)

• Developed AI-enhanced workflow automation capabilities for enterprise service management products, integrating intelligent processing features into cloud-based business applications through reliable backend services and APIs.
• Built Python-based automation services supporting AI integrations, improving application workflows by connecting machine learning components with enterprise platforms through secure REST interfaces and scalable service patterns.
• Implemented container-based development and deployment workflows using Docker and Kubernetes concepts, improving application consistency across development environments and reducing configuration-related deployment issues.
• Supported cloud platform reliability by improving logging, monitoring, and operational diagnostics for AI-enabled services, helping maintain stable performance for enterprise workflow automation solutions.
• Integrated automated testing and CI/CD validation processes into software delivery workflows, improving release confidence through repeatable builds, quality checks, and controlled deployment procedures.
• Worked in Agile engineering teams to develop technical documentation, troubleshoot production issues, and collaborate with product teams delivering secure AI-driven enterprise automation features.

Data Engineer - Suvoda

(2015-08 - 2019-12)

• Developed data processing solutions for clinical trial technology platforms supporting patient-centric applications, creating reliable pipelines for collecting, transforming, and validating healthcare study information.
• Built Python and SQL-based ETL workflows for clinical data integration, improving data consistency across trial management systems while supporting secure handling of sensitive healthcare information.
• Supported application reliability by maintaining scheduled processing jobs, database workflows, and deployment scripts while improving operational stability of data services used by clinical teams.
• Contributed to Agile software delivery by testing data workflows, documenting technical processes, and collaborating with engineers to improve scalability and maintainability of healthcare technology solutions.

Bachelor of Computer Science - Computer Science - Politehnica University of Timișoara (2009 - 2013)

Master of Software Engineering - Software Engineering - Politehnica University of Timișoara (2013 - 2015)