Senior Agentic AI Engineer

O projekcie:
- Remote working- Comprehensive premium health insurance- Sports club membership- My Benefit – cafeteria system access- Competitive compensation package- Support for tuition and continued education- Active sports and social events committee

Wymagania:
We are looking for an experienced Senior Agentic AI & Harness Engineer to join a technology-driven organization developing advanced AI solutions for complex enterprise environments.

The role focuses on building scalable, secure, and production-ready AI agent systems that automate business processes, integrate with enterprise applications, and support operational decision-making.

You will play a key role in designing the infrastructure and engineering frameworks that enable AI agents to operate reliably in real-world environments. Working closely with software engineering, data, cloud, and security teams, you will help establish technical standards and reusable solutions for enterprise AI adoption.

- At least 5 years of professional software engineering experience, including hands-on experience delivering LLM-powered or agent-based solutions in production.- Advanced Python programming skills and strong knowledge of modern software engineering practices.- Practical experience developing AI agents, orchestration frameworks, and LLM-based applications.- Solid understanding of retrieval-augmented generation (RAG), context engineering, memory management, and tool-calling mechanisms.- Experience implementing asynchronous processing, API integrations, stateful workflows, and fault-tolerant systems.- Strong knowledge of AWS cloud services and experience working with managed LLM platforms such as Amazon Bedrock or similar technologies.- Experience designing automated tests, evaluation pipelines, and regression testing for AI-powered applications.- Understanding of application security, authorization mechanisms, and secure integration patterns.- Familiarity with CI/CD pipelines, version control, and production deployment practices.- Strong problem-solving skills and the ability to troubleshoot complex distributed systems.- Good communication skills and experience collaborating with multidisciplinary engineering teams.Nice to have:- Experience working in financial technology, enterprise software, or regulated industries.- Knowledge of Model Context Protocol (MCP), model gateways, and advanced tool integration architectures.- Experience with multi-agent frameworks and workflow orchestration technologies.- Familiarity with containerization, infrastructure as code, and cloud-native architectures.- Knowledge of AI governance, responsible AI practices, and model risk management.- Experience building internal AI platforms, shared engineering libraries, or reusable development frameworks.- Relevant cloud, AI, or security certifications.- Experience providing technical guidance or mentoring other engineers.

Codzienne zadania:
- Design, develop, and maintain production-grade AI agent architectures and execution frameworks.
- Build agent orchestration mechanisms covering task planning, context management, retrieval, memory, and tool interaction.
- Develop reliable workflows incorporating state persistence, checkpointing, retry strategies, error recovery, and execution safeguards.
- Integrate LLM-based solutions with internal applications, APIs, databases, and enterprise services.
- Implement security and governance mechanisms, including access controls, approval workflows, input validation, and protection against prompt injection.
- Ensure appropriate separation between AI-driven decision-making and deterministic business logic.
- Create automated testing and evaluation frameworks to assess agent accuracy, reliability, tool usage, and overall performance.
- Establish monitoring and observability capabilities to track system behavior, execution quality, latency, and operational costs.
- Identify opportunities to improve agent performance, scalability, and maintainability.
- Collaborate with cross-functional teams to deliver AI solutions from initial design through production deployment.
- Contribute to architectural decisions, technical documentation, and engineering best practices.