LA
Senior Modernization Engineer (FDE)
Kyndryl
Wrocław, PLSênior6 de jul.
O que falta para esta vaga
Palavras-chave extraídas da descrição real — educativo, não garante entrevista.
Esta vaga menciona estes termos com frequência:
fullstackModernizationFDEreacttypescriptpythondockerawsmongodb
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fullstackModernizationFDE
Sobre a vaga
- Key Responsibilities
- Building autonomous agents using LLMs, planning algorithms, and decision-making frameworks.
- Implementing agent architectures that support autonomy, interactivity, and task completion.
- Integrating agents into applications, APIs, and workflows (e.g., copilots, chatbots, automation tools).
- Connecting agents to external services via APIs, databases, and cloud platforms.
- Tuning agent behavior using feedback loops, reinforcement learning, semantic knowledge layer and user interaction.
- Monitoring performance and implementing safety, reliability, and guardrail mechanisms.
- Working cross-functionally with researchers, engineers, and product teams.
- Maintaining clear documentation of agent logic, designing decisions, and dependencies.
- Building and maintaining the Enterprise Agents and Tools Registry for metadata and lifecycle management.
- Implementing the Agent Communication Gateway with robust security, rate limits, observability, and cost controls.
- Familiarity with AI Agent orchestration patterns and workflow orchestration engines (e.g., Temporal, Airflow, etc.).
- Ensuring agent-level security, including authentication, authorization, and data protection.
- Optimizing cost, scalability, performance, and reliability of agent operations across cloud and on-prem environments.
- Familiarity with knowledge graphs for agent reasoning and data integration.
- Deploying and customizing agentic AI platforms (e.g., LLM agents, orchestration frameworks).
- Integrating AI systems with enterprise APIs, data platforms, and workflows.
- Solving technical blockers across data ingestion, model deployment, and agent behavior.
- Designing and refining prompts to ensure clarity, compliance, and contextual accuracy.
- Translating business logic into agentic workflows and task trees.
- Tuning agent behavior to align with real-world expectations.
- Implementing observability tools to ensure reliability, latency, and trustworthiness.
- Maintaining performance metrics and feedback loops for continuous improvement.
- Building and iterating custom AI solutions tailored to customer needs, leveraging agentic AI frameworks
- Owning delivery end to end, from scoping to production. Working as part of the customer team to engineer and deploy production-ready solutions that drive adoption and measurable business outcomes.
- Actively contributing to the evolution of Kyndryl’s AI platforms through feedback, code contributions, and collaboration with product team
- Customer Engagement & Solution Integrity: Partnering with customers to understand business
- Bachelor’s degree in computer science, Engineering, or equivalent.
- Hands-on experience with Python development and frontend UI technologies (e.g., TypeScript, React.js, etc.) to build demos/systems from scratch as a full stack engineer.
- Hands-on experience building AI based solutions using AI frameworks such as LangChain, Microsoft Semantic Kernel, Google ADK or Microsoft Agent Framework.
- Knowledge of LLMs, AI Agent architectures, Agent Telemetry/Observability frameworks (Langsmith, Langfuse, litellm etc).
- Expertise with Docker, Kubernetes, and at least one cloud platform (Azure, AWS, GCP) or on premises.
- Experience in microservices-based architectures.
- Solid grasp of the software delivery lifecycle, version control (Git & GitHub), and data engineering tools such as Pandas and Spark.
- Experience with cloud AI platforms (AWS, Azure, Google AI) and distributed computing architectures.
- Ability to translate business requirements into technical solutions and communicate technical value to diverse stakeholders, including executive audiences.
- Hands-on experience with SQL (e.g., PostgreSQL), NoSQL (e.g., MongoDB), and vector databases for agent data storage, semantic queries and retrieval.
- Proficiency in CI/CD (e.g., GitHub Actions), automated testing, and observability.
- Familiarity with agent-based modeling, multi-agent systems, or reinforcement learning.
- Proficiency in API development, backend services, and cloud platforms (AWS, Azure, GCP).
- Practical knowledge of deploying RAG architectures and integrating structured and unstructured knowledge sources into AI solutions.
- Open-Source Ecosystems: Familiarity with community-driven AI tools and libraries, including Hugging Face and relevant repositories.
- T-shaped Profile: Deep technical expertise in one or two domains, with broad understanding across AI/ML, cloud, and consulting.
- Agentic AI Systems: Experience designing, building, or integrating multi-agent systems and orchestration frameworks (e.g., LangGraph, Semantic Kernel, Agent Framework, AutoGen, CrewAI), including the development of agent protocols and coordination mechanisms.
- Performance & Security: Knowledge of system-level optimisation and security best practices for scalable AI systems.
- Willingness to travel up to 25% globally.
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