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Microservices & API Engineering: Design, build, and deploy cloud-native backend services, RESTful APIs, orchestration layers, and event-driven architectures.
AI & GenAI Integration: Architect Retrieval-Augmented Generation (RAG) systems, integrate LLMs and vector databases, and build AI agent workflows.
Ontology & Knowledge Graphs: Model taxonomies, design ontologies, and manage semantic relationships between enterprise entities using graph technologies (RDF, OWL, SPARQL).
Cloud Infrastructure & DevOps: Deploy microservices on AWS using Docker, Kubernetes, CI/CD pipelines, and Infrastructure as Code (IaC) tools.
Reliability & Testing: Build automated unit, integration, and contract tests, while managing production monitoring, logging, alerting, and incident response.
Cross-Functional Delivery: Collaborate with product owners, enterprise architects, and data engineers to convert complex domain requirements into scalable backend solutions.
Core Language Mastery: Strong backend proficiency in at least one key stack: Java, Python, Node.js/TypeScript, or .NET/C#.
Architecture & APIs: Hands-on experience with API-first development, RESTful microservices, event-driven systems, and distributed service orchestration.
AWS Cloud Ecosystem: Deep expertise with AWS core services, containerization (Docker, Kubernetes), and CI/CD automated deployment pipelines.
Software Engineering Standards: Solid grounding in secure coding, secrets management, automated testing frameworks, and production observability.
Semantic Tech Expertise: Hands-on experience with Ontology Modeling, Knowledge Graphs, RDF, OWL, SPARQL, or graph databases (e.g., Neo4j, Neptune).
GenAI/LLM Architecture: Practical experience implementing RAG solutions, prompt engineering, vector databases (e.g., Pinecone, Milvus, Qdrant, Pgvector), and AI evaluation frameworks.
Experience using AI-assisted coding tools (GitHub Copilot, Cursor) within enterprise software engineering workflows
Bachelor's degree
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