Engineer ai állások, munkák X. kerületben

• Design and implement automation of data platform
• Develop and maintain data platform infrastructure using Terraform
• Implement and manage CI/CD pipelines for automation of data and AI processes and pipelines
• Automate provisioning, configuration, and monitoring of resources in AWS

• Designing and developing new machine learning models with a focus on real-time performance
• Integrating cutting-edge ML features into the SkeleTRACK product
• Analysing model performance and identifying areas of improvement
• Mentoring junior engineers and driving technical discussions

• Project-specific security design for data-driven and AI-powered systems including cloud environments on AWS, Azure, and GCP
• Risk assessment with threat modeling focusing on data security and vulnerabilities related to LLMs and AI models

• End-to-end implementation of data pipelines and GenAI applications on Databricks
• Design and maintenance of CI/CD, model serving, monitoring, and evaluation frameworks for AI production reliability
• Tuning of complex Spark jobs and LLM inference patterns for performance optimization

• Building and maintaining infrastructure for large-scale data and video streaming platforms
• Supporting development teams by making deployments easier, faster, and more reliable
• Improving security by helping to monitor and prevent potential risks

• Design and development of a brand new cricket tracking system
• Ownership of tracking pipelines across the full system
• Definition and evolution of system architecture
• Driving robustness, accuracy and performance in live sports environments

• Leading the design and evolution of ball and player tracking algorithms using multi-camera systems
• Owning multi-view geometry solutions across entire tracking pipelines
• Driving improvements in occlusion handling, recovery and system robustness in live sports environments

• Building and evolving CI infrastructure validating performance methodologies across cloud, on-prem, and hardware targets
• Designing controlled, reproducible test environments to distinguish real regressions from environmental noise

• Maintaining and evolving AI/ML projects using Python
• Building design Agentic LLM based solutions and maintaining/improving existing ones
• Ensuring output quality by verifying consistency and accuracy
• Investigating, assessing, and developing cloud based MCP solutions

• Setting technical direction across teams
• Driving AI-native and cloud-native architecture and long-term system evolution
• Leading design of secure, scalable MCP hosts and AWS microservices
• Balancing performance, reliability, and cost in system design

• Development, simulation, and optimization of manufacturing and production systems
• Manufacturing and process simulation using Tecnomatix Plant Simulation and Process Simulate
• Modeling of material flow, logistics processes, cycle times, resource utilization, and bottlenecks

• Facilitation of onboarding and offboarding of engineers to project and resource management systems
• Troubleshooting access issues related to project management platforms and systems
• Maintenance and updating of engineer profiles and certifications
• Review and verification of weekly timecards

Ownership of larger functionality of Case Platform Design and implementation of backend services using Java 17+, Spring Boot, and modern architectural patterns Contribution of production-quality code Performance of design reviews Driving architectural decisions Ensuring reliability and operational...

• Design and own secure, scalable services
• Guide technical decisions and evaluate trade-offs including AI-driven components
• Architect and deliver AI/agentic product features including LLM integrations, multi-step agentic workflows, tool-use patterns, RAG pipelines, and intelligent automation

• Design Azure cloud infrastructure to support Databricks and data platform workloads
• Implement Azure cloud infrastructure to support Databricks and data platform workloads
• Manage Azure cloud infrastructure to support Databricks and data platform workloads

• Building and evolving the Risk Manager platform
• Developing secure, scalable microservices using TypeScript and React in a serverless architecture on AWS
• Contributing to AI-powered features for risk management
• Taking end-to-end ownership of services from development to deployment and monitoring

• Building secure, scalable serverless microservices and AI-powered features using TypeScript and React on AWS for the Risk Manager platform
• Owning and delivering well-defined stories independently with a clear grasp of team architecture, frameworks, and tools

• Design and own secure, scalable services
• Guide technical decisions and evaluate trade-offs including AI-driven components
• Leverage AI tools to accelerate coding, debugging, testing, and prototyping
• Rigorously validate AI outputs to ensure quality and safety

• Planning, creating, executing, and maintaining automated test cases
• Analyzing and documenting the results of automated tests
• Reporting defects
• Collaborating closely with cross-functional team members
• Contributing to the continuous improvement of testing processes

• Az Archicad alacsony szintű rétegeinek fejlesztése és karbantartása Windows és macOS platformokon
• A termék licenszelési és jogosultságkezelési rétegének fejlesztése, beleértve a cloud alapú megoldásokat
• Hálózati kommunikációval kapcsolatos kliens oldali fejlesztések