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Niche agent engineering plus broad fullstack and ML skillset yields moderate applicant competition.
Highly specialized agent and ML systems skills reduce cross-industry transferability.
Extensive mandatory tech stacks and specialized agent/ML expertise imply rigorous filtering.
Design and build AI agents, their orchestration workflows, and RAG solutions focusing on chunking strategies and controlled vocabularies.
Develop backend services and APIs using Java, Spring Boot, Kafka, MongoDB; build frontend dashboards/UI using React and TypeScript.
Deploy cloud-native AI solutions on AWS and Kubernetes with a focus on prompt engineering, evaluation, and model reasoning frameworks.
Strong Python skills with asyncio and async/await experience.
Experience with agent architectures, MCP, RAG implementations, prompt engineering, and OpenAI/Anthropic agent frameworks.
Proficient in Java 17+, Spring Boot, MongoDB, Kafka; understanding of event-driven architectures and distributed system patterns.
Work Experience Required: Not explicitly mentioned in the JD
Experienced in building enterprise AI platforms or agent-based applications with strong backend distributed system knowledge.
Comfortable working across AI agent engineering, backend, frontend, and cloud-native deployment technologies.
Deep understanding of distributed system consistency, reliability patterns, and modern AI agent orchestration frameworks.