Ai engineer állások, munkák Budapesten

• Komplex LLM-, RAG- és agentikus AI-megoldások célarchitektúrájának megtervezése, szakmai irányítása és produkciós bevezetése.
• Coding agent skillek, MCP-szerverek, agent harness és runtime komponensek, routing-, memory-, tool- és evaluációs megoldások kialakítása.

• End-to-end LLM-, RAG- és agentikus AI-megoldások megtervezése, fejlesztése, tesztelése és produkciós bevezetése.
• Coding agent skillek, MCP-szerverek, promptkomponensek, subagentek és többlépéses workflow-k kialakítása; agent harness komponensek fejlesztése.

Agentic AI development: Design and implement autonomous and semi-autonomous AI workflows, including planning, task execution, tool integration, context management, and fault recovery....

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

• Megtervezed és a csapattal közösen felépíted az agentic AI-platformot, amelyre a fejlesztési munkafolyamatainkat alapozzuk.
• Coding agenteket és AI-toolchaineket integrálsz a meglévő C#/.NET és Vue.js ökoszisztémánkba, kialakítva az önálló és többagentű működés feltételeit.

AI Governance Policy and responsibilities: Review and update the Mol Group AI Governance policy, future operational guidelines and role responsibilities with the AI Governance Lead, drawing on approved regulatory guidance and lessons learned. Track how teams put the policy into practice, report...

• Designing and building agentic systems that plan, call tools and automate real business workflows
• Designing and building multi-agent (agent-to-agent) solutions in which agents hand work to each other, review it and escalate to a human when needed

• Develop, integrate and operate scalable AI applications
• AI agents and intelligent assistance systems
• Implement AI use cases from proof of concept through production
• Advise business teams
• Define reusable AI standards and architectures, and ensure secure, stable and cost-effective production operation

Developing and maintaining mission-critical video processing, transcription, accessibility pipelines, and interactive analytics services using TypeScript, React, and Ruby on Rails...

• Design, build and deploy AI agents and multi-agent pipelines for B2B clients across sales, support, and operations
• Architect integrations between LLM layers, voice AI (VoIP/SIP), CRMs, and business workflows

• Build and maintain Azure Databricks solutions
• Develop scalable ETL/ELT pipelines
• Implement and optimize Lakehouse solutions
• Develop Spark applications
• Develop reusable data and AI platform components
• Build and maintain MLOps pipelines using Azure Databricks
• Automate platform, data and ML deployments

• Integrating delivered components into working embedded systems
• Debugging firmware, drivers, and application-level issues
• Adapting and extending example code and reference designs
• Building internal demos and proof-of-concept applications
• Giving structured, technical feedback based on real usage

Driving modeling and experimentation for SEON's fuzzy matching and AML screening quality, turning ambiguous problems into well-defined approaches and carrying them from experiment through release and operation...

• Building and operating deployment pipelines for AI services, covering CI/CD, infrastructure-as-code, environment promotion, and rollback
• Deploying and operating model serving, batch scoring, and orchestration pipelines across development, staging, and production

Participating in owning the quality architecture for the Scoring engine, prioritizing test strategies based on real-world risk and standardizing reusable patterns for the repository...

Building and maintaining the GRC Brain (AI reasoning layer) and Agentic Cortex (agent orchestration framework) — the technical core of the Compliance in a Box product...

• Designing and building agentic systems that plan, call tools and automate real business workflows
• Developing LLM-powered applications, including RAG pipelines: chunking, embedding, retrieval strategies, evaluation and iteration

• Building and optimizing inference pipelines for large-scale model serving (LLMs and beyond)
• Working with frameworks like PyTorch, TensorRT, and vLLM to deploy models efficiently

• AI támogatói rendszer készítése IT security munkákhoz és penetrációs tesztekhez, hogy a rendszer alapvizsgálatokat tudjon végezni és a vizsgálatokból komplett jelentéseket készítsen on-prem
• Saját minták feltanítása az általunk használt adatbázisokból

Designing and implementing AWS-native contact center and AI agent solutions based on Amazon Bedrock AgentCore and Amazon Connect from the initial concept through to stable production operations...