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AI and data engineering professional focused on building intelligent, scalable, and automated business solutions. I design end-to-end AI/ML and data pipelines using Python, SQL, and modern cloud-native architectures to enable reliable real-time analytics and decision-making. I lead production-grade AI systems and automation workflows, covering model integration, governance, monitoring, and infrastructure-as-code. I also collaborate across teams to define architecture standards and deliver measurable impact using DevOps practices with Docker, Kubernetes, Terraform, and cloud platforms.…AI and data engineering professional focused on building intelligent, scalable, and automated business solutions. I design end-to-end AI/ML and data pipelines using Python, SQL, and modern cloud-native architectures to enable reliable real-time analytics and decision-making. I lead production-grade AI systems and automation workflows, covering model integration, governance, monitoring, and infrastructure-as-code. I also collaborate across teams to define architecture standards and deliver measurable impact using DevOps practices with Docker, Kubernetes, Terraform, and cloud platforms.

Sadhna Singh

AI Engineer, DevOps Developer, Data Engineer, +2

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AI and data engineering professional focused on building intelligent, scalable, and automated business solutions. I design end-to-end AI/ML and data pipelines using Python, SQL, and modern cloud-native architectures to enable reliable real-time analytics and decision-making. I lead production-grade AI systems and automation workflows, covering model integration, governance, monitoring, and infrastructure-as-code. I also collaborate across teams to define architecture standards and deliver measurable impact using DevOps practices with Docker, Kubernetes, Terraform, and cloud platforms.…AI and data engineering professional focused on building intelligent, scalable, and automated business solutions. I design end-to-end AI/ML and data pipelines using Python, SQL, and modern cloud-native architectures to enable reliable real-time analytics and decision-making. I lead production-grade AI systems and automation workflows, covering model integration, governance, monitoring, and infrastructure-as-code. I also collaborate across teams to define architecture standards and deliver measurable impact using DevOps practices with Docker, Kubernetes, Terraform, and cloud platforms.

Available to hire

AI and data engineering professional focused on building intelligent, scalable, and automated business solutions. I design end-to-end AI/ML and data pipelines using Python, SQL, and modern cloud-native architectures to enable reliable real-time analytics and decision-making.

I lead production-grade AI systems and automation workflows, covering model integration, governance, monitoring, and infrastructure-as-code. I also collaborate across teams to define architecture standards and deliver measurable impact using DevOps practices with Docker, Kubernetes, Terraform, and cloud platforms.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

Artificial Intelligence Engineer at EdditX Smart AI Solutions

July 1, 2025 - Present

Designed and implemented AI-enabled calculation and analytics systems converting complex rule-based logic into scalable, data-driven processing. Built automated data ingestion and transformation pipelines using Python and SQL to integrate multiple source systems and improve data quality, efficiency, and reliability. Developed high-performance computation engines for large-scale transactional volumes with complex business logic. Created near real-time analytics views and dashboards for actionable insights. Implemented automated validation and confirmation workflows via intelligent forms and reporting layers to reduce manual effort and improve traceability for thousands of users.

Python Developer at T-pro Info Solutions

April 1, 2023 - June 30, 2025

Built and maintained CI/CD pipelines to automate build, test, and deployment workflows for AI models and backend services. Provisioned and managed cloud infrastructure using infrastructure-as-code for scalable and secure AI workloads. Containerized AI applications with Docker and orchestrated deployments using Kubernetes for reliable runtime operations. Developed data ingestion, preprocessing, and feature engineering pipelines using Python and SQL to support analytics and machine learning. Integrated AI models into production using APIs and batch processing. Implemented monitoring, logging, and alerting for infrastructure and AI workloads, and automated environment management, secrets handling, and security controls for compliant deployments.

Software Engineer at Avensys Consulting UK

August 1, 2020 - March 31, 2023

Designed, provisioned, and managed cloud infrastructure using Terraform with reusable modules and infrastructure-as-code practices to ensure consistency and scalability. Built and maintained containerized applications using Docker, creating optimized images and standardizing build processes. Deployed and operated Kubernetes clusters to run microservices and backend workloads with high availability, scalability, and fault tolerance. Implemented CI/CD pipelines for containerized application and infrastructure changes. Managed Kubernetes workloads (deployments, services, config maps, secrets, ingress controllers, autoscaling). Automated environment provisioning and release workflows to reduce manual intervention. Implemented monitoring, logging, and alerting for containerized and Kubernetes systems, and handled secure access management, secrets rotation, and environment configuration across dev/staging/prod.

Associate Software Engineer at B2B Holidays

March 1, 2017 - July 31, 2023

Built, containerized, and maintained applications using Docker with standardized images and optimized build processes. Deployed, operated, and scaled applications on Kubernetes, managing workloads, services, ingress, secrets, and autoscaling. Implemented and maintained CI/CD pipelines across multiple environments. Automated environment provisioning, configuration management, and release processes to reduce manual effort and deployment risk. Monitored system health, performance, and availability using logging, monitoring, and alerting and responded to production issues. Ensured infrastructure and application security through access controls, secrets management, network policies, and compliance practices.