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• Design, build, review, test, and maintain Ruby on Rails features and services in line with Diligent’s agile engineering practices
• Own technical delivery for projects, epics, and major user stories, including solution design, implementation planning, and execution through to production

• Development and customization of Microsoft Dynamics 365 applications using C#/.NET and Power Platform tools
• Building integrations between Dynamics 365 and Azure services such as Logic Apps and Functions

• Development, customization, and maintenance of ServiceNow applications, modules, and integrations
• Collaboration with cross-functional teams to understand business requirements and translate them into technical solutions

• Design, implement, and manage Azure cloud infrastructure to support Databricks and data platform workloads
• Build and maintain Azure resources including Resource Groups, VNets, Key Vaults, Storage Accounts, and Azure Active Directory (AAD) integrations

• Designing and building AWS infrastructure and landing zones
• Defining AWS infrastructure using Terraform or OpenTofu with Terragrunt, or AWS CloudFormation
• Building CI/CD pipelines
• Leading migrations of customer workloads from on-premises, GCP, Azure, or Heroku to AWS

• Architecting complex AWS infrastructures and zero-day compliant landing zones
• Understanding customers’ business problems and providing technology consulting to identify the best-fit solution
• Owning the end-to-end solution as a key decision-maker and driving the delivery team

• In-depth assessments with clients to understand business and technical needs
• Analysis of client requirements and proposal of feasible technical solutions
• Identification of client challenges and goals with recommendation of effective solutions

• Technical management of a team of 2-3 AI and cloud engineers focused on fast prototyping of AI/ML solutions in AWS cloud
• Mentoring and technical support to junior and medium teammates
• Acting as a trusted advisor to clients
• Development of highly scalable services realizing AI/ML functionalities

• Odoo-modulok fejlesztése és testreszabása Python 3, XML, HTML használatával
• Backend-szolgáltatások fejlesztése Java/Spring és Hibernate segítségével
• Külső rendszerek összekapcsolása REST- és XML-RPC-API-k segítségével
• Eseményalapú integráció az Apache Kafka segítségével

Shaping AWS architectures and technical decisions for web applications, SaaS platforms and enterprise environments...

• Identification of highest-leverage AI opportunities in developer workflow
• Ownership of foundational architecture and standards for agent modeling and internal tool evaluation and deployment
• Building and improving onboarding, documentation, and discovery layers for engineers

• Partnering with engineering teams to automate and optimize service availability, scalability, performance, monitoring, and alerting
• Developing software to automate the toil required to keep SaaS offerings highly available, scalable, and resilient

• Building and maintaining simulation environments for mobile robotic perception and navigation
• Developing and integrating robotic behaviours and learning pipelines for reinforcement learning and motion planning in complex outdoor environments

• Aktív részvétel a tervezésben
• Specifikációk finomítása
• Fejlesztési feladatok megvalósítása
• Code review
• Automatikus tesztelés
• CI/CD folyamatok karbantartása (GitLab)
• Monitorozás és riasztások kezelése
• Alkalmazás karbantartása Kubernetes környezetben
• Junior fejlesztők mentorálása
• 1on1 meetingek tartása

• Vezetés a szoftverfejlesztés és tesztelés tervezésében, fejlesztésében, tesztelésében és telepítésében
• A teljes szoftverfejlesztési életciklus irányítása a követelménygyűjtéstől a terméktámogatásig

• Planning, implementation, development and testing of HW/SW components for the company backbone and data center networks
• Preparation of technical feasibility studies, design proposals and execution of POCs for the networks

• Adatfolyamok tervezése és fejlesztése robusztus, skálázható és biztonságos batch és real-time ETL/ELT folyamatok fejlesztésével
• Adatmodellezés telekommunikációs adatmodellek, adattárházak és adattavak logikai és fizikai tervezésével, struktúrájának kialakításával

• Development, testing, and continuous improvement of cloud-native CaaS and PaaS capabilities primarily using Go
• Contribution to Kubernetes operators and controllers that automate cluster and platform lifecycle processes

• Design, develop, test, and continuously improve cloud-native CaaS and PaaS capabilities primarily using Go
• Develop Kubernetes operators and controllers to automate cluster, platform, and application lifecycle processes

• Building and maintaining production-grade AI infrastructure using Infrastructure as Code, monitoring, logging, and deployment automation
• Optimising performance and cost efficiency of LLM workloads through autoscaling, caching, batching, and infrastructure right-sizing