Help us shaping what the Future can be

Role

We are looking for an Architect / DevOps Engineer to join GovHorizon's engineering team. You will own our cloud infrastructure, automation, CI/CD pipelines, and DevOps practices — with a focus on reliability, scalability, and continuous delivery.

Responsibilities

• Design, build, and maintain cloud and on-premise infrastructure.

• Build and optimize CI/CD pipelines for applications and microservices.

• Automate resource provisioning using Infrastructure as Code (Terraform, Ansible).

• Work with development teams to embed DevOps practices across the product.

• Monitor system performance, handle incidents, and improve operational continuity.

• Contribute to infrastructure architecture standards and best practices.

• Document workflows, configurations, and operational guides to ensure reproducibility and maintainability.

Requirements

• 3–5 years of experience in DevOps, Infrastructure Engineering, or a similar technical role.

• Hands-on experience with at least one major cloud provider (Azure, AWS, or GCP).

• Solid understanding of CI/CD, automation, and containers (Docker / Kubernetes).

• Experience with IaC tools (Terraform, Ansible, or equivalent).

• Ability to communicate clearly with development teams and stakeholders.

Role

We are looking for a Machine Learning / AI Engineer to help build, improve, and operationalize AI capabilities and intelligent models within the GovHorizon platform — from data preparation through to production deployment.

Responsibilities

• Build, train, and optimize ML/AI models for strategic use cases (e.g. regulatory forecasting, text analysis, predictive intelligence).
• Prepare and clean data, run feature engineering, and validate input quality for models.
• Work with engineering teams to deploy models to production (APIs, MLOps pipelines).
• Monitor model performance and implement automated retraining, drift detection, and performance metrics.
• Document workflows, model architecture, and experiment results.
• Help operationalize MLOps practices and model lifecycle management tools.

Requirements

• 3–5 years of experience in Machine Learning, Data Science, or AI Engineering.
• Proficiency in Python and ML libraries (scikit-learn, TensorFlow, PyTorch, or equivalent).
• Knowledge of data pipelines, ETL/ELT, and orchestration frameworks (Airflow, Kubeflow, etc.).
• Experience with cloud ML services and deployment (Azure ML, Databricks, AWS SageMaker, GCP Vertex AI).
• Solid understanding of model performance metrics and result validation.