Agentic AI Engineer

⇒ Role Summary This role sits at the intersection of AI research, software engineering, and system design, with a strong focus on agent-based architectures and real-world deployment. Beyond core technical expertise, our client is seeking a candidate who is curious, experimental, and forwardthinking. ⇒ Main Responsibilities • Design and implement agentic AI systems capable of planning, reasoning, and executing multi-step tasks. • Develop and optimise AI agent workflows, including tool usage, memory management, and orchestration. • Comfortable with Remote Procedure Calls (RPC) such as gRPC for integrating with software ecosystem. • Implement real-time decision engines and intelligent automation frameworks. • Evaluate and improve agent performance through testing, benchmarking, and iteration. • Contribute to system architecture, ensuring robustness, scalability, and security. • Stay current with the latest advancements in agentic AI, LLMs, and autonomous systems, and apply them where relevant. ⇒ Qualifications & Experience • Bachelor's Degree in Information Technology or relevant fields. • At least 1–2 years of relevant work experience; fresh graduates are welcome. • Strong programming expertise in Python. • Proficiency in at least one of the following: C, C++, Golang, or Java. • Hands-on experience with agentic AI frameworks and workflows (e.g., LangChain, AutoGen, CrewAI, Semantic Kernel, or similar). • Solid understanding of LLMs and prompt engineering | Multi-agent systems and orchestration patterns | API integration and distributed systems. • Experience building production-grade AI systems or microservices. • Strong problem-solving skills and ability to work in complex, evolving environments. • Experience with real-time data processing or sensor-driven systems. • Familiarity with decision engines, reinforcement learning, or planning algorithms. • Knowledge of cloud platforms (Azure, AWS, or GCP) and containerized deployments such as docker. • Experience working with vector databases, embeddings, and retrieval systems. ⇒ Research & Innovation Expectations • Actively keeps up with recent research trends in Agentic AI, LLM orchestration, and autonomous systems. • Evaluates and prototypes emerging techniques from academic and industry research. • Brings forward new ideas and improvements to enhance system capabilities. ⇒ Key Attributes • Strong ownership mindset and ability to deliver end-to-end solutions. • Curiosity-driven with a passion for emerging AI technologies. • Ability to balance research insights with practical implementation. • Effective communicator with a collaborative approach.