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• Design, develop, test, document, and deploy high quality Salesforce solutions leveraging Einstein 1 Platform, Lightning Web Components, and modern Apex patterns
• Analyze client specific requirements and define data model to support business, reporting and technical requirements

• Delivery of software releases on schedule with defined scope and quality
• Transition to continuous delivery
• Ensuring reliable operation of CI/CD pipelines and improving their efficiency
• Contribution to test strategy and coordination of its execution
• Development of test frameworks and automation tools

• CI/CD-folyamatok tervezése, felépítése és üzemeltetése (Azure DevOps, GitLab CI, GitHub Actions vagy Jenkins)
• Infrastruktúra kezelése kód formájában (Terraform, Bicep/ARM, Ansible)
• Konténeres környezetek felépítése és üzemeltetése (Docker, Kubernetes / AKS)

• Build, operate, and improve production data pipelines using Python, Airflow, Kubernetes, and related technologies
• Design scalable data pipelines and data models for growing data volumes and processing needs

• Implementation and maintenance of large-scale data pipelines and the Azure/Databricks data platform
• Building robust, scalable pipelines using Azure Databricks (PySpark / SQL)
• Design and enforcement of data architecture standards including Delta Lake / Lakehouse architecture and data modeling

• AI-Native Infrastructure: Modern, GPU-intenzív AI/ML keretrendszerek szoftveres alapjainak megtervezése és kiépítése.
• Deep Containerization: Komplex Docker image-ek faragása, kifejezetten GPU-kompatibilis, AI workload-specifikus erőforráskezelésre (NVIDIA-docker, CUDA optimalizáció).

• Magas szintű technikai és üzleti tanácsadás ügyfelek számára az Atlassian termékeivel és saját fejlesztésű megoldásokkal kapcsolatban
• Ügyfelek üzleti és IT folyamatainak felmérése
• Komplex megoldások tervezése, bevezetése és minőségbiztosítása

• 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

• Setup and maintenance of IT cloud infrastructure for multiple products
• Setup and maintenance of the automated build/deployment jobs
• Maintenance of various support tools and utilities
• Monitoring application performance and resource planning

• 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

• Leading and growing multiple agile engineering teams
• Defining and owning the technical roadmap with stakeholders
• Championing responsible use of AI and automation
• Aligning technical strategy with business goals
• Mentoring and coaching engineers at all levels

• 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

• Chunking and ingestion architecture with deterministic, validation-gated pipelines handling diverse enterprise document types with indexing and contextual enrichment
• Retrieval pipeline with vector search, BM25 hybrid retrieval, RRF re-ranking, embedding model selection and optimisation

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...

• Shaping AWS-based cloud architectures and technical decisions for SaaS, PaaS, web application, and enterprise cloud environments
• Design and implementation of Infrastructure as Code solutions using tools such as Terraform, AWS CloudFormation, or Pulumi

• Identification of highest-leverage AI opportunities in the 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

• Delivery of agentic solutions across multiple client deployments
• Building and configuring full-stack AI-enabled solutions
• Embedding with client service teams and client teams to understand needs
• Translating client needs into technical and functional requirements

• Developing scalable library software using modern tools and languages for various numerical method
• Performance tuning, optimization, and benchmarking of algorithms on various architectures