Python állások, munkák XI. kerületben

• Komplex AI megoldások fejlesztése ügyfelek számára
• RAG alapú megoldások fejlesztése Python/LangChain vagy Java/Spring AI technológiákkal
• Open source LLM-ek integrálása ügyfélrendszerekbe cloud és on-premise környezetben Kubernetes használatával

• Build and maintain scalable cloud infrastructures using modern services and best practices
• Proactive detection, analysis, and fixing of vulnerabilities and performance bottlenecks in cloud environments

• Data ingestion processes from various sources into the Data Lake
• Design, development and optimization of complex data pipelines for reliable data flow
• Build, development and maintenance of frameworks facilitating construction of data pipelines

• Lead the design and implementation of scalable data pipelines and Lakehouse architectures
• Act as the design authority
• Solve the hardest technical challenges including optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks

• Experience in different data science topics
• Independent delivery of data science projects with e2e product-oriented mindset
• Direct engagement with clients to understand business objectives and translate into technical requirements
• Leading pre-sales technical discussions

• Technical onboarding of TelCo and OSS applications onto Kubernetes-based platforms
• Contribution to Site Reliability Engineering through automation of operational tasks and creation of enhancements

• Analysis of webshop and digital campaign performance across multiple brands and channels
• Building and maintaining campaign benchmarking databases using historical performance data
• Development of forecasting methodologies and performance estimates for future campaigns

• Build and maintain dataset ingestion, cleaning, and versioning pipelines
• Design and operate labeling workflows including quality control and inter-annotator agreement tracking
• Own dataset testing including coverage analysis, class balance, leakage detection, and dataset documentation

• Integration of security best practices into the DevOps lifecycle
• Implementation and continuous improvement of security services, CI/CD pipelines, and cloud infrastructure with focus on automation and scalability

• Leadership in integration of security best practices into DevOps lifecycle
• Architecting, implementing, and continuously improving security services, CI/CD pipelines, and cloud infrastructure
• Design and enforcement of secure configuration management, secrets handling, RBAC, and network policies

• Design, build, and operate enterprise-grade Agentic AI platforms
• Own architecture and delivery
• Mentor engineers
• Partner closely with client and internal teams
• Run secure AI environments at scale under strict regulatory and security requirements
• Architect Zero-Trust environments for LLM-based solutions

• 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 through model deployment and monitoring

• Training and fine-tuning models against curated datasets
• Running systematic hyperparameter and architecture experiments
• Building and maintaining evaluation suites
• Analyzing model failures and translating findings into dataset or training improvements

• Design, build, and launch AI and generative AI solutions
• Create scalable data pipelines and model deployment workflows
• Develop large-language-model and multi-agent products
• Set up automated testing, deployment, monitoring, and improvement

• Design and develop scalable cloud security automation solutions across multi-cloud environments
• Build and maintain platform services using modern engineering practices and a code-first approach
• Own and enhance CI/CD pipelines that enable secure, reliable and efficient delivery

Ownership of detection capabilities end to end from problem framing and data through experimentation to production models and telemetry...

• Építés agentic rendszerek tool-using munkafolyamatokkal, RAG, strukturált kimenetek, guardrails és mérhető minőség
• Együttműködés Solution Architect-ekkel és LLMOps-szal
• Fejlesztés megbízható és folyamatosan fejlesztett rendszerekben

• End-to-end penetration testing engagements across web applications, APIs, cloud environments, kubernets, binary applications and internal/external infrastructure
• Simulation of real-world attacks to identify security risks before they become threats

• Design and implement cloud security controls across AWS, Azure, and/or GCP
• Deploy and tune Cloud Security Posture Management and Cloud-Native Application Protection Platform tooling
• Build security into Infrastructure-as-Code and CI/CD pipelines

• Designing and developing proof of concepts (PoCs)
• Demonstrating solutions to internal customers on a tight schedule
• Utilizing GenAI no code, low code, and SDKs to build robust GenAI agents that automate business processes
• Taking GenAI and Agentic AI solutions from concept to Proof-of-Value (PoV)