Data Engineer (II)

Agent Architecture & Development: Design and implement production-grade multi-agent AI solutions (including automated meta-agents and specialized orchestration agents) using modern frameworks optimized for enterprise cloud infrastructure.

Master’s degree in computer science, a related technical field, or equivalent practical experience. Professional experience in software development, including building large-scale, asynchronous data pipelines using Kubernetes, Docker, or industry equivalents like Celery, Airflow, or Kafka. Extensive experience with Python programming and standard software engineering development practices. Practical experience working with enterprise cloud services (AWS, Google Cloud Platform, or Azure) and containerized infrastructure orchestration. Demonstrated proficiency in schema validation (e.g., Pydantic, Protobuf) and large-scale AI evaluations using precision/recall, F1 score, or LLM-as-a-judge methodologies. Experience setting up golden datasets, designing rapid AI experiments, and deploying production AI agents using enterprise Agent Development Kits (ADKs) or AI SDKs., Experience working with generative AI and Large Language Models (LLMs), including expertise in advanced prompting techniques, fine-tuning, and evaluation. Background in designing and deploying multi-agentic systems, autonomous architectures, and modern agentic orchestration frameworks (e.g., LangGraph, CrewAI, AutoGen, or LlamaIndex). Experience with tool-calling protocols, including Model Context Protocol (MCP), API integrations, and function-calling schemas. Experience with vector databases and vector search integrated with enterprise cloud databases for RAG architectures at scale. Strong understanding of distributed systems and performance optimization in high-growth, high-volume data environments.