Containerização Jobs

Senior Python Developer – Containerized Geospatial Processing Library (Azure) ​Project Overview: We are seeking an experienced Senior Python Developer with deep expertise in geospatial data handling (GIS) and Docker containerization. The objective of this project is to build a robust, standalone Python library and execution environment that performs standardized geoprocessing tasks on vector and raster datasets. ​The final deliverable must be a highly secure, containerized microservice capable of reading from and writing to Azure Blob Storage, with strict input validation. ​Core Tech Stack: ​Language: Python 3.11+ ​Geospatial Libraries: GeoPandas, Rasterio, Shapely, PyProj, Fiona ​Validation: Pydantic ​Cloud: Azure SDK (Blob Storage, Key Vault, Identity) ​Infrastructure: Docker (Alpi...

I need a containerlab expert who can be my parter in setting up and managing a library of labs. I do topology discovery. I need SNMP, gNMI, Netconf and REST access to the lab from my desktop. Initially, each lab will be static. Eventually, I would like labs that change topology over time. Labs in GitHub Codespaces would be optimal but willing to consider other options.

Project Overview I am building a B2B AI automation platform for small and medium businesses. The platform monitors email and calendar, surfaces intelligent summaries and recommended actions, automates repetitive GUI workflows across web applications, and gives teams an AI assistant accessible via a Chrome extension sidebar. This is a production build — not a prototype. The platform needs to be ready for real paying customers at the end of the engagement. What You Will Build The full product, end to end. This includes: • A Python backend API deployed on a cloud platform, handling authentication, AI routing, cost management, and all business logic • A Chrome Manifest V3 browser extension with a sidebar interface — the primary user surface • Continuous ...

I’m building a virtual DeepseekV3 environment that emulates Jet Nano hardware for research and development on machine-learning models. The goal is to give my team a sandbox where we can move seamlessly from data preprocessing and feature extraction through model training, evaluation, deployment, and monitoring—without touching the physical board until we are ready. Here’s what I need: • A reproducible simulation that mirrors Jet Nano’s CUDA-enabled GPU, memory constraints, and I/O. • Containerised tool-chain (PyTorch, TensorRT, cuDNN, etc.) with scripts that cover the full life-cycle: preprocessing, training, hyper-parameter sweeps, evaluation metrics, and a mock-deployment stage that tracks resource usage and latency. • Clear documentation so a...

I'm seeking an experienced DevOps engineer to enhance our CI/CD pipelines. We currently use Jenkins and GitLab CI/CD, with code repositories hosted on GitHub. Key Responsibilities: - Improve and maintain CI/CD pipelines using Jenkins and GitLab CI/CD. - Collaborate with development teams to ensure seamless integration and deployment. - Monitor and troubleshoot pipeline issues. Ideal Skills and Experience: - Proficiency in Jenkins and GitLab CI/CD. - Strong GitHub knowledge. - Experience in scripting and automation. - Familiarity with containerization (Docker, Kubernetes) is a plus. - Excellent problem-solving skills and teamwork.

I am building a backend service that acts as a decision layer between an internal platform and multiple external service providers. This is not a simple integration bridge. The goal is to design a scalable, extensible decision engine that: • Normalises heterogeneous API responses into a canonical internal schema • Applies configurable business rules to determine optimal outcomes • Abstracts external providers behind adapter contracts • Allows new providers to be added without modifying core logic The first external provider integration will serve as a reference implementation. However, the architecture must support rapid onboarding of additional providers via a pluggable adapter model. ⸻ Core Requirements 1️⃣ Canonical Data Model Design a clean internal schema...

Replace Datadog With OSS Stack We spend about €1,400 each month on Datadog and want to eliminate that cost by running an open-source observability stack—Prometheus for metrics, Loki for logs, Alertmanager for notifications and Grafana for visualisation—inside our existing Kubernetes cluster. Today Datadog covers three things for us: monitoring & alerting, log management and detailed performance metrics. I need the same coverage after the migration, and I need it in real time; anything slower is a non-starter. I also have several dashboards in Datadog that the team relies on every day. Those dashboards must be migrated into Grafana with the same look, data sources and alert thresholds. Deliverables Helm (or Kustomize) manifests that deploy Prometheus, Loki, Alertmanage...

My container is deployed on Cloud Run and attached to a VPC via a Serverless VPC Access connector that in turn goes through Cloud NAT with a public IP. A custom domain is mapped as well. Despite the setup, the service cannot reach the public internet—most visibly the MongoDB Atlas cluster. I have already reviewed firewall rules, confirmed the NAT configuration, and inspected container logs, yet outbound traffic still fails (other external endpoints time-out too). What I need from you • Pinpoint the exact root cause of the egress blockage—routing, connector, IAM, or anything else—so I understand why it broke. • Apply the fix live during a screen-sharing session; no code transfer will be necessary. • Prove success by showing my backend log line that ...

I need a seasoned AWS architect who can, within the next 24 hours, help me decide whether to keep our CPU- and GPU-intensive workloads at Hetzner or migrate to AWS for better performance. The immediate objective is a clear, data-backed recommendation focused on performance improvement; if the numbers prove compelling, we will move straight into Phase 2 (implementation) tomorrow. Here is what I expect from Phase 1: • A side-by-side cost and performance comparison of Hetzner versus AWS, covering on-demand, Savings Plan and Spot pricing for EC2, projected container costs on ECS/EKS, and any Lambda-based serverless bursts we might leverage. • A proposed high-level architecture that shows which workloads belong on EC2, where container orchestration under EKS/ECS makes sense, and t...