Senior Genai Solutions Engineer

Role Description & Performance Metrics. The Senior GenAI Solutions Engineer will design, develop, and deploy advanced Generative AI and Agentic solutions for Konecta’s covering both enterprise processes as well as industry solutions. These are experienced professionals, expected to contribute from day one with strong coding skills, AI / ML expertise, and a proven ability to independently deliver production-grade pilots and prototypes while adhering to Konecta’s engineering and security standards. Metrics : First working prototype delivered within 4 x weeks of onboarding. At least 2 x horizontal AI pilot use cases transitioned to live usage per quarter. Reusable and standardized code modules created in line with Konecta best practices. All code and solutions delivered in line with Konecta InfoSec and compliance standards. Positive feedback from internal horizontal stakeholders and the CTO GenAI. Continuous market scanning focused on Gen AI and Agentic AI vendors. Responsibilities : Build and industrialize GenAI solutions (assistants, automation flows, RAG / agent pipelines) for both internal and external use. Write production-quality Python code that complies with Konecta best practices and delivers reusable standard components. Autonomously gather, analyze, and evaluate requirements from internal stakeholders for AI / agentic use cases. Assess whether use cases require single-agent or multi-agent architectures. Implement agent-based solutions leveraging well-known frameworks (AutoGen, CrewAI, LangGraph, SmolAgents, etc.). Ensure security by design : apply secure coding, data protection, and access controls across all solutions. Connect GenAI solutions and applications with enterprise systems and APIs (ERP, HRS, Finance platforms, databases etc). Independently develop PoCs and pilots to validate use cases before scaling. Ensure full compliance with Konecta AI governance (GDPR, EU AI Act, DORA, InfoSec policies). Document solutions, create demo assets, and contribute to internal accelerators for reuse. Profile 6–10 years of professional experience in software engineering & ML / AI. 2 years hands-on in Generative AI, with proven delivery of RAG / agent-based and agentic deployments. Proven track record of DIY pilots, PoCs, or demonstrable project portfolio showing ability to deliver independently end-to-end. Expert-level Python coder with strong ML / AI frameworks knowledge (PyTorch, TensorFlow, Hugging Face, LangChain / LangGraph). Skilled in API integration and cloud-native deployment (Azure, GCP, or AWS). Strong engineering discipline (CI / CD, Git, testing, documentation, secure coding practices). Fluent English, spoken and written, to collaborate with global teams and stakeholders. Keeps updated with latest developments in GenAI, MCP, and agentic frameworks (via GitHub, conferences, research papers, vendor updates). Has experience with, or is open to using, AI coding platforms (e.G., GitHub Copilot, Claude Code, Cursor) to accelerate delivery. Understands AI security risks (prompt injection, data exfiltration, access control) and applies mitigation in PoCs and production pilots. Able to autonomously evaluate requirements for agentic solutions (single-agent vs multi-agent setups) and apply well-known agent frameworks in delivery. Professional mindset : self-driven, delivery-oriented, and comfortable working in fast-paced, global environments.