Senior Modernization Engineer (FDE)

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. • Private health card • Life insurance package • Multisport Card • Discounts for Kyndryl employees • A wide range of benefit options for parents and children • Sports activities and team events • Free language courses & many personal development possibilities • Employee referral program • Mindfulness and Yoga Classes