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Develop and implement AI agents and multi‑agent systems capable of autonomously executing tasks, making context‑driven decisions, and interacting with external tools, APIs, databases, and external services.
Architect and orchestrate multi‑step, multi‑agent workflows, decomposing high‑level goals into executable tasks and ensuring seamless communication, reliable tool use, and efficient coordination across complex pipelines.
Implement logic for LLM‑driven decision‑making, including tool selection, tool chaining, fallback strategies, guardrails, and overall agent resilience.
Build and maintain evaluation and observability frameworks to measure agent accuracy, reliability, and efficiency, while continuously monitoring production behavior and improving system robustness.
Develop memory, retrieval, and RAG systems using vector databases or knowledge graphs to enhance agent context, reasoning, and long‑term performance.
Deploy, scale, and manage agent‑based solutions on cloud platforms such as AWS, Azure, and GCP.
Collaborate with cross‑functional teams—including product, domain experts, engineering, and security—to ensure agent capabilities align with business requirements and operational constraints.
Requirements
Bachelor’s degree in computer science, mathematics, applied statistics, various engineering disciplines, or related STEM discipline
0-2 years of experience in cloud engineering or a related field.
Relevant experience can be considered as a substitute for the required educational qualifications.
In the absence of a degree, a minimum of 4 years of related experience is required.
Basic programming skills in Python, C++, C# or Java.
Understanding of foundational machine learning concepts and techniques.
Strong analytical and problem-solving skills.
Proficiency in prompt engineering and context design for reasoning and tool orchestration
API and tool integration skills (REST, GraphQL, microservices).