Senior AI-Native Engineer

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About the project

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Your responsibilities

• Architect and develop AI-native applications where intelligent agents and LLM-powered components serve as foundational building blocks
• Design and implement multi-agent systems capable of autonomous reasoning, planning, tool selection, and execution of complex workflows
• Integrate Large Language Models into production environments, ensuring reliability, observability, and graceful degradation
• Embed AI throughout the software development lifecycle — from AI-assisted design and code generation to intelligent testing, review, and deployment automation
• Develop orchestration layers that manage agent communication, shared memory, context windows, and task delegation
• Craft and iterate on prompt engineering strategies to drive consistent, high-quality model outputs across diverse use cases
• Evaluate and benchmark emerging AI models, frameworks, and agent platforms to inform technology decisions
• Design feedback loops, guardrails, and evaluation mechanisms to ensure AI system safety, accuracy, and alignment
• Collaborate with cross-functional teams — including product managers, data engineers, and platform architects — to deliver AI-driven solutions at scale
• Contribute to the creation of internal standards, reference architectures, and reusable patterns for AI-native development
• Mentor engineers across teams on agentic design patterns, LLM integration best practices, and AI-first thinking
• Continuously monitor the evolving AI landscape and translate emerging research into practical engineering approaches

Our requirements

• Minimum of 5 years of professional software engineering experience with a demonstrated focus on AI-powered systems
• Proven, hands-on experience in AI Agents development or LLM integration — building and shipping applications where large language models drive autonomous reasoning, content generation, or complex decision-making
• Experience with AI orchestration frameworks such as Spring AI or LangChain4J for structured development of agent-based applications
• Strong Prompt Engineering skills — including designing prompt templates, chains, and system instructions that produce reliable, context-aware model behavior in production settings
• Proficiency in Python for AI/ML prototyping, model interaction, scripting, and working within the broader AI development ecosystem
• Hands-on experience with AWS or GCP cloud platforms, particularly services supporting AI/ML workloads (e.g., Bedrock, SageMaker, Vertex AI, Lambda, Cloud Functions)
• Demonstrated practice of embedding AI into the SDLC — actively using AI-powered assistants, agents, or copilots to accelerate and enhance software development workflows
• Strong system design skills with the ability to architect solutions that combine traditional services with autonomous AI components
• Critical thinking and ability to assess model limitations, hallucination risks, and appropriate use cases for AI automation
• Excellent communication skills and ability to work effectively in international, cross-functional Agile teams
• Proficiency in English (B2+), both written and spoken

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