Program állások XXIII. kerületben (ai és automatizáció)

• Execution and support of cross-product validation frameworks from the business and financial logic perspective
• Identification of quality gaps and recommendation of improvements aligned with firm-wide standards
• Ensuring AI-driven workflows meet defined quality thresholds

• Design and build the automated review pipeline with triage, security, quality, and architecture passes
• Build and maintain deployment gates that block critical vulnerabilities across every deploy surface

• Design and implement APIs and domain services that abstract the complexity of using an LLM and enable rapid feature development from product teams
• Build scalable AWS infrastructure for LLM interactions, including prompt management, context handling, auditing, and response processing

Development and enhancement of the MSCI Index Development Platform and the IndexMetrics Real-Time application using modern software engineering practices, Python, Azure, and AI-assisted development tools...

• Building and maintaining SQL queries and reports that track bookings, pipeline coverage, and conversion
• Monitoring CRM data quality to resolve issues at the source
• Analyzing pipeline trends and supporting the weekly forecasting review cadence with clean, accurate data

• Managing the project lifecycle delivery of Sustainability & Climate Corporate Solutions for the Corporates client segment
• Serving as the dedicated client liaison during each project engagement

• Build and operate deployment pipelines for AI services
• Deploy and operate model serving, batch scoring, and orchestration pipelines
• Partner with data scientists and applied AI engineers for system design and production deployment

• Design and build learner models including knowledge tracing and longitudinal approaches
• Shape the data foundation for learner modeling including defining relevant signals and building datasets
• Translate mastery and progression definitions into model targets and evaluation criteria

• Ownership of data models and storage architecture behind AI systems across relational, graph, and retrieval-oriented data
• Building and operating APIs, backend services, and pipelines to keep data current and accessible

• Owning net-new pipeline and revenue across the service lines
• Running and sharpening the outbound engine
• Managing and developing an SDR
• Refining sequences and account-based plays
• Keeping the tooling stack tuned with an external GTM partner
• Setting the bar for activity and conversion

• Owning a revenue target across data, advanced analytics, and predictive AI
• Leading executive-level client conversations to diagnose business problems, structure solutions, and build ROI cases

• Design and build artificial intelligence and machine learning solutions for enterprise clients
• Operationalize AI and machine learning solutions combining custom models with cloud and third-party AI services
• Assess client needs and data for AI/ML solutions

• Design and implement automation for storage provisioning, capacity management, and operational workflows using Python, Bash, and IaC tooling
• Drive reduction of manual toil through scripted workflows and self-service enablement

• Design and deliver technical trainings, workshops and enablement sessions for customers, partners and internal teams
• Explain complex cloud architectures, concepts and T Cloud Public services in a clear and structured way
• Develop and maintain training materials, hands-on labs and certification content

• Development of the test automation framework
• Development of test cases/steps
• Development of test tools
• Integration of testing tools into the automation framework

• End-to-end implementation of automation solutions from initial idea assessment through to retirement
• Assessment of new use cases and mapping of business processes
• Identification of key process improvements

• Business process analysis and design of Lead-to-Order processes
• Cross-country process harmonisation
• Requirements engineering
• Business workshops and stakeholder alignment
• Fit-gap and impact analysis
• End-to-end solution definition
• Backlog readiness and delivery support
• Business validation

• Operational and financial management of the EMEA Marketing Operations & MarTech Hub
• Creation of processes, visibility and insight for managing market demand, Hub performance and A&P
• Consolidation of information across markets and Hub capabilities into clear insights and recommendations for leadership

• Development and fine-tuning of AI models including LLMs, Agentic AI, and Generative AI systems
• Building and deploying robust AI applications and services on cloud platforms
• Design and implementation of efficient AI pipelines from data preparation to model deployment and monitoring

• Leadership in integration of security best practices into DevOps lifecycle
• Architecture, implementation and continuous improvement of security services, CI/CD pipelines and cloud infrastructure
• Design and enforcement of secure configuration management, secrets handling, RBAC and network policies