AI Engineer - Agentic

Role overview

Responsibilities

• check_circle Architect, build, and deploy agentic AI systems that integrate AI agents with foundation models, enterprise systems, third-party tools, and APIs using established agentic frameworks or custom-built orchestrators.
• check_circle Build and maintain retrieval-augmented generation (RAG), memory, and reasoning pipelines that ground agent decisions in reliable, real-world data and enable persistent, contextual, and adaptive behavior.
• check_circle Design single-agent and multi-agent workflows, including planning, delegation, tool selection, handoffs, inter-agent communication, and human-in-the-loop controls.
• check_circle Optimize orchestration and reasoning performance while balancing autonomy, latency, cost, interpretability, reliability, and maintainability.
• check_circle Collaborate with Generative AI engineers, application engineers, MLOps engineers, and product teams to move agentic AI solutions from prototype to production.
• check_circle Monitor, evaluate, and benchmark agent performance, and help ensure AI systems are safe, accurate, trustworthy, observable, and capable of delivering a high-quality user experience.
• check_circle Document agent architectures, communication flows, guardrails, context-engineering strategies, memory design, tool interfaces, and orchestration logic to ensure clarity and reproducibility.
• check_circle Stay current with advances in agentic AI, multi-agent orchestration, RAG, model-context protocols, cognitive architectures, evaluation methods, and AI safety mechanisms.
• check_circle Collaborate with cross-functional teams to design and deliver complete AI solutions for customers.

Basic qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related field.
• 2+ years of hands-on experience developing agentic AI systems, LLM-powered applications, or advanced Generative AI solutions.
• Preferably 5+ years of overall professional software engineering experience
• Contributions to open-source agentic AI frameworks, orchestration platforms, evaluation tooling, or agent communication protocols.
• Experience building microservices and deploying AI applications on cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP).
• Experience with AI safety, guardrails, observability, traceability, evaluation, and explainability for agentic AI systems.
• Experience deploying LLM or agentic workloads in production using containers, Kubernetes, CI/CD pipelines, and modern MLOps or LLMOps practices.