Senior Agentic AI Engineer

O projekcie

Project description

Spyrosoft continues its rapid growth by expanding its advanced AI and Agentic capabilities. We are now looking for an experienced Senior Agentic AI Engineer to join our Artificial Intelligence Unit and help shape the future of intelligent, agent-driven products and platforms. In this role, you will work at the forefront of Generative AI and Agentic Systems, leading the design and implementation of advanced AI solutions across industries. You will collaborate with agile teams to build innovative end-to-end AI pipelines - from data preparation and model deployment to the development of autonomous AI agents that can reason, plan, and interact with tools and enterprise systems. This is a hands-on, delivery-focused role with a strategic dimension - perfect for someone who thrives at the intersection of cutting-edge technology, business impact, and scalable engineering.

About Spyrosoft

Spyrosoft is an authentic, cutting-edge software engineering company, established in 2016. In 2021 and 2022, we were among the fastest growing technology companies in Europe, according to the Financial Times. We were founded by a group of tech experts with established backgrounds in software engineering, who created an ‘engineer-to-engineer’ workplace, powered by enthusiasm, fairness and authentic relationships. Having a unique offering, which bridge the gap between technology and business, we specialise in technology solutions for industry 4.0, automotive, geospatial, healthcare & life sciences, employee experience & education and financial services industries.

Zakres obowiązków

• Design, develop, and deploy Agentic AI solutions using platforms such as Azure OpenAI Services, Google Vertex AI, Amazon Bedrock, or similar.
• Build and orchestrate AI agents and multi-agent workflows, leveraging modern agentic frameworks (e.g., LangChain, LangGraph, Semantic Kernel).
• Develop intelligent applications that combine LLMs, retrieval, tool execution, and memory, enabling agents to perform complex tasks autonomously.
• Implement robust RAG pipelines, knowledge grounding, and enterprise search integrations to support reliable agent behavior.
• Engineer production-grade agent evaluation, monitoring, guardrails, and safety mechanisms to ensure trust and alignment.
• Work in cross-functional agile teams to deliver end-to-end AI systems - from exploration and feature engineering to training, validation, deployment, and continuous improvement.
• Investigate and analyze large, high-dimensional datasets, applying the latest AI/ML techniques to extract value and generate actionable insights.
• Serve as a technical advisor during client engagements, identifying opportunities, recommending solutions, and sharing best practices in GenAI and agentic architectures.

Wymagania

• MSc in Artificial Intelligence, Data Science, Computer Science, Engineering, Mathematics, or a related field.
• A few years of hands-on experience in applied machine learning or AI engineering roles.
• Solid proficiency in Python programming
• Strong experience with AI platforms such as Azure OpenAI, Google Vertex AI, Amazon Bedrock, or similar.
• Practical knowledge of building AI agents, including tool-use, reasoning workflows, planning loops, and autonomous task execution.
• Familiarity with agentic frameworks and orchestration tools such as LangChain, LangGraph, Semantic Kernel, or similar.
• Experience implementing RAG pipelines, knowledge grounding, and enterprise integrations for agent-based applications.
• Familiarity with deploying AI systems into production, including MLOps/LLMOps practices, evaluation strategies for LLMs and agents, and cloud ML ecosystems (Azure, AWS, GCP).
• Experience with Databricks.
• Demonstrated ability to handle complex, high-volume, multi-source data effectively.
• Strong communication skills and business orientation, with the ability to translate technical AI outputs into strategic value for clients and stakeholders.
• Proficiency in spoken and written English.
• Polish B2 is required.
• Nice to have: experience with ML/data libraries such as scikit-learn, pandas, PyTorch, or TensorFlow, along with an understanding of deep learning and classical ML techniques.