Yael Acceptic

We are looking for a Platform Engineer to join the team — a persone who collaborates, innovates, and treats every problem as a mission to be completed. You will help build and operate the internal platform that other engineering teams depend on: GitOps delivery, Kubernetes-based runtime, infrastructure automation, and developer self-service capabilities. You will use AI and Agentic AI as everyday tools to accelerate everything you do — code generation, automation, troubleshooting, documentation, and platform tooling.

About:

Our client is developing electro-mechanical devices for dairy farms, pulsators and automatic detachers. The solution is leading in the industry with advanced cow monitoring solutions based on activity and rumination sensors.

Responsibilities:

     Platform Engineering (Core):

• Build and operate the internal platform: GitOps delivery, Kubernetes runtime, infrastructure automation, and developer self-service capabilities;
• Apply platform-as-product thinking — product teams are your customers; developer experience is the metric;
• Contribute to the team's evolution toward IDP capabilities on Port, Crossplane, and self-service control planes;
• Maintain and extend Infrastructure as Code (Terraform / OpenTofu) for platform components;

• Ship POCs that turn into platform capabilities.

   

  AI & Agentic AI in Daily Work:

• Use AI coding assistants (Claude, Copilot, or similar) for code, IaC, and platform automation;
• Design and use agentic AI workflows to automate repetitive platform tasks — investigation, remediation, documentation, scaffolding;
• Build AI-augmented tooling into the platform itself where it accelerates product teams;

• Set the bar for the team on responsible, effective use of AI in engineering work.

   

  Cloud, AWS & Networking (Foundational):

• Operate confidently in AWS: EKS, IAM, S3, RDS, Lambda, CloudWatch;
• Networking proficiency is required: VPC design, subnets, CIDR planning, route tables, security groups, NACLs, peering, Transit Gateway, VPC endpoints, DNS (Route53);
• Troubleshoot connectivity, ingress/egress, and cross-account/cross-VPC traffic flows;

• Apply networking knowledge to Kubernetes: Services, Ingress, Network Policies, CNI behavior.

   

  GitOps & Kubernetes (Day-One Operational):

• Operate ArgoCD as a core platform capability: GitOps workflows, application/infrastructure delivery, multi-environment promotion;
• Own ApplicationSets-driven multi-version, branch, and PR deployment patterns — providing the platform capability while product teams consume it;
• Deploy, troubleshoot, and instrument Kubernetes workloads;

• Apply progressive delivery patterns (canary, blue-green) where they fit.

   

  GitHub Org & Artifact Management (Platform-Owned):

• Manage the GitHub organization as a platform service: orgs, teams, repos, templates, code owners, branch and environment protections, secrets, OIDC to AWS.
• Build and maintain reusable GitHub Actions workflows that product teams consume out of the box;
• Operate CI bots for PR validation, auto-labeling, auto-merge on green, and policy enforcement;
• Curate Copilot org policies and custom Copilot instructions so AI assistance is consistent and safe across repos;

• Operate JFrog Artifactory as the artifact backbone: registries, retention, promotion paths, Xray scanning, and signed artifacts flowing through environments.

   

  Collaboration & Leadership:

• Collaborate with CloudOps, DevSecOps, FinOps, and SRE — peer-to-peer, not siloed;
• Mentor team members; deliver POCs, technical presentations, and documentation;
• Bring innovation to the team: new tools, new patterns, new ways of working;
• Champion shared standards across the platform community.

Requirements:

• 7+ years in Platform Engineering, SRE, or closely related roles;
• Platform mindset — you build platforms and self-service capabilities; you do not run a ticket queue;
• AI fluency: hands-on daily use of AI coding assistants and agentic AI workflows to deliver engineering work;
• AWS proficiency: EKS, IAM, S3, RDS, Lambda, CloudWatch, and the operational fundamentals;
• AWS networking: VPC, subnets, CIDR planning, route tables, security groups, NACLs, peering / Transit Gateway, VPC endpoints, Route53 — confident designing and troubleshooting multi-VPC topologies;
• Kubernetes: strong operational proficiency — deploying, troubleshooting, and instrumenting workloads (not cluster architecture);
• ArgoCD & GitOps: multi-environment Kubernetes deployments, ApplicationSets, progressive delivery;
• Terraform / OpenTofu: proficiency with declarative infrastructure management;
• CI/CD: tool-agnostic pipeline design (Azure DevOps, GitHub Actions, or both);
• GitHub as a platform: org/team/repo administration, reusable Actions workflows, branch and environment protections, OIDC to AWS, CI bots for PR validation and auto-merge, and Copilot org policies / custom Copilot instructions;
• Artifact management: hands-on with JFrog Artifactory (or directly comparable, e.g., Nexus, GitHub Packages at scale) — registries, retention, promotion, vulnerability scanning;
• Scripting: Python, Go, Bash, or similar for automation and tooling;
• Team player and assertive collaborator: works effectively across Platform, CloudOps, DevSecOps, FinOps, and SRE functions; takes initiative and finishes missions;
• IDP tools (Stack Port, Backstage) — central to where the team is heading;
• Crossplane for Kubernetes-based infrastructure management;
• Argo ecosystem depth (Rollouts, Workflows, Events);
• SRE practices: SLOs, SLIs, error budgets, incident response, post-incident reviews;
• DORA metrics: defining, tracking, and driving measurable delivery improvement;
• Building or operating internal AI-powered developer tools;
• At least Upper-Intermediate English level.

Would be a plus:

• Observability: Datadog, OpenTelemetry, Prometheus & Grafana — instrumentation, dashboards, alerting (a Plus, not a requirement);
• Service mesh (Istio, Linkerd);
• FinOps practices and cloud cost optimization tooling;
• Policy as Code (OPA, Kyverno);
• Certifications: CKA/CKAD, AWS Solutions Architect/DevOps Engineer/Advanced Networking, HashiCorp Terraform.

We offer:

Professional Development: Free English courses, free access to the corporate Udemy account, corporate digital library subscription, possibility to participate and share your knowledge as a speaker in our internal meetups.

Benefits: 100% paid vacation and sick leaves, opportunity to accumulate part of the own salary in the company in dollar terms.

We’re looking for a Mobile Automation Developer to join our team and help build reliable, scalable services.
 

About:

Our client is developing electro-mechanical devices for dairy farms, pulsators and automatic detachers. The solution is leading in the industry with advanced cow monitoring solutions based on activity and rumination sensors.

Responsibilities:

• Design and implement QA test automation infrastructure and test scenarios for client–mobile–backend systems, including communication between devices, front-end, back-end services, and mobile applications;
• Design and execute end-to-end (E2E) tests for iOS and Android, testing new features while focusing on deep internal aspects of the feature and its integration with the operating system;
• Learn and adopt new features, technologies, and platforms in a relatively short amount of time (e.g., Bugfender, Raygun, Crashlytics, Datadog).

Requirements:

• Bachelor’s degree in Computer Science, Engineering, or another exact science field;
• 4+ years of experience writing automated tests for mobile devices, using Python (UI automation tests, integration tests);
• Experience working with iOS testing tools such as Instruments, XCUITest, XCTest, or Xcode;
• Experience working with Android testing tools such as Instrumented Tests, Local Tests, Espresso, or Android Studio;
• At least 1 year of experience in automation infrastructure development;
• Experience working with RESTful APIs;
• Advanced English (written and spoken).

Would be a plus:

• Experience writing mobile unit tests on devices or emulators;
• Experience with mobile automation frameworks such as Robot Framework or Cucumber using Python;
• Experience with one of the following systems: AWS Device Farm, Sauce Labs, WebdriverIO, Docker, AWS, Airflow, BrowserStack App Automate;
• 1 year of manual web testing experience;
• Experience working with or building CI/CD pipelines and understanding the mobile automation development cycle (Jenkins, GitHub Actions, Azure AppCenter);
• In-depth knowledge of mobile operating systems;
• Experience testing Mobile SDKs, location-driven features, NFC, or BLE integrations;
• Experience testing battery consumption in background processes.
   

We offer:

Professional Development: Free English courses, free access to the corporate Udemy account, corporate digital library subscription, possibility to participate and share your knowledge as a speaker in our internal meetups.

Benefits: 100% paid vacation and sick leaves, opportunity to accumulate part of the own salary in the company in dollar terms.

We are seeking an experienced AI Edge Device Developer to join our dynamic team. The ideal candidate will be responsible for designing, developing, and optimizing AI algorithms and models for deployment on edge devices. Excellent problem-solving skills, attention to detail, and the ability to work in a fast-paced environment are essential for success in this role. If you are passionate about pushing the boundaries of AI at the edge and thrive in a collaborative environment, we encourage you to apply.
 

About:

Our client is developing electro-mechanical devices for dairy farms, pulsators and automatic detachers. The solution is leading in the industry with advanced cow monitoring solutions based on activity and rumination sensors.

Responsibilities:

• Designing, developing, and optimizing computer vision pipelines for deployment on edge devices;
• Cross-compiling solutions for various CPU/NPU/VPU types. Perform performance analysis and optimization to ensure real-time processing capabilities on edge devices;

• You will face real-life challenges and complexities emerging from working closely with our AI researchers to provide market leading products.

   

Requirements:

• 5+ years experience in Python, C/C++;
• Hands-on experience with GStreamer or DeepStream;
• Experience with TensorRT, ONNX;
• Experience with processing multiple video streams;
• Hand-on experience with ML accelerators, such as: Nvidia Jetson, Google Coral;
• Proficient in Linux;
• Proficient in Git version control;
• Bachelor's Degree in Computer Engineering, Computer Science or related fields;

• At least Upper-Intermediate English level.

   

Would be a plus:

• Understanding of concepts in signal processing, machine learning and computer vision;
• Familiarity with machine learning frameworks such as TensorFlow, PyTorch;
• Neural network models quantization and pruning;
• Familiarity with building Docker images (Docker files);
• Understanding of CI/CD processes for production code, unit tests, and regression testing;

• Knowledge of CUDA.

   

We offer:

Professional Development: Free English courses, free access to the corporate Udemy account, corporate digital library subscription, possibility to participate and share your knowledge as a speaker in our internal meetups.

Benefits: 100% paid vacation and sick leaves, opportunity to accumulate part of the own salary in the company in dollar terms.