Advanced GenAI Solutions Developer

About the Role Imagine designing and developing innovative solutions that bring together Generative AI and Agentic technologies. As a Senior GenAI Solutions Engineer, you will play a pivotal role in shaping the future of our organization.

What We Offer Benefits and opportunities available to join cutting-edge GenAI and Agentic technologies teams.

Main Responsibilities Design and develop high-quality GenAI solutions using cutting-edge frameworks such as AutoGen, CrewAI, LangGraph, and SmolAgents.

Collaborate with cross-functional teams to gather requirements and assess the feasibility of agentic solutions.

Implement agent-based architectures and ensure seamless integration with existing systems.

Maintain and improve security by design, applying secure coding practices and data protection measures.

Develop Proof-of-Concepts (PoCs) and pilots to validate use cases before scaling.

Requirements 6-10 years of experience in software engineering and ML / AI.

2+ years hands-on experience in Generative AI, with proven delivery of RAG / agent-based and agentic deployments.

Expert-level Python coding skills, with knowledge of popular ML / AI frameworks like PyTorch, TensorFlow, Hugging Face, and LangChain / LangGraph.

Strong understanding of API integration, cloud-native deployment, and DevOps practices.

Excellent communication and collaboration skills, with fluency in English.

Able to stay updated with the latest developments in GenAI, MCP, and agentic frameworks.

Your 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.

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