Z
Senior AI Engineer
Zenith System Solutions
5 hours ago
Full-time
On-site
Plano, Texas, United States
8+ years of experience in software engineering with strong expertise in Python.
Hands-on experience designing and developing microservices-based architectures using FastAPI or similar Python frameworks.
Experience building scalable, secure, and production-grade APIs.
Strong experience with Microsoft Azure, including AI, data, and application services.
Expertise in MongoDB and NoSQL database design, optimization, and data modeling.
Experience deploying and managing cloud-native applications and containerized workloads.
Generative AI & Agentic Systems
Hands-on experience building AI Agents, multi-agent systems, and autonomous workflows. Strong understanding of Retrieval-Augmented Generation (RAG) architectures, including:
Knowledge RAG Graph RAG Hybrid retrieval systems Context-aware retrieval pipelines
Experience designing enterprise knowledge bases, semantic search solutions, and context-building frameworks. Strong understanding of Large Language Models (LLMs) and Small Language Models (SLMs). Experience with model selection, prompt engineering, grounding strategies, fine-tuning approaches, and inference optimization. Familiarity with model performance, latency, cost optimization, and governance considerations. AI Evaluation & Observability
Hands-on experience implementing Agentic Evaluation frameworks, including tools such as DeepEval. Answer relevance Agent task completion Safety and reliability Experience with AI observability and monitoring platforms, particularly Arize AI, for production monitoring, tracing, and model performance analysis. Preferred Qualifications
Experience with vector databases and embedding models. Familiarity with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks. Experience building enterprise AI applications with governance, security, and compliance considerations. Knowledge of MLOps/LLMOps practices, CI/CD pipelines, and model lifecycle management. Key Responsibilities
Design and develop scalable AI-powered microservices and APIs using Python and FastAPI. Build and maintain enterprise-grade RAG and Graph RAG solutions. Develop intelligent agentic workflows that leverage organizational knowledge and business context. Optimize knowledge retrieval, context orchestration, and response quality for AI applications. Implement evaluation frameworks and monitoring solutions using DeepEval and Arize. Deploy, monitor, and maintain AI applications on Azure. Collaborate with product, engineering, and business teams to translate use cases into production-ready AI solutions. Must Have Existing Hands-On Experience
Semantic Kernel LangGraph Neo4j or Graph Databases Kubernetes and Docker GitHub Actions / Azure DevOps Additional Note:
This version is targeted at a S Generative AI Engineer (7-10+ years experience).
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Hands-on experience building AI Agents, multi-agent systems, and autonomous workflows. Strong understanding of Retrieval-Augmented Generation (RAG) architectures, including:
Knowledge RAG Graph RAG Hybrid retrieval systems Context-aware retrieval pipelines
Experience designing enterprise knowledge bases, semantic search solutions, and context-building frameworks. Strong understanding of Large Language Models (LLMs) and Small Language Models (SLMs). Experience with model selection, prompt engineering, grounding strategies, fine-tuning approaches, and inference optimization. Familiarity with model performance, latency, cost optimization, and governance considerations. AI Evaluation & Observability
Hands-on experience implementing Agentic Evaluation frameworks, including tools such as DeepEval. Answer relevance Agent task completion Safety and reliability Experience with AI observability and monitoring platforms, particularly Arize AI, for production monitoring, tracing, and model performance analysis. Preferred Qualifications
Experience with vector databases and embedding models. Familiarity with LangChain, LangGraph, Semantic Kernel, LlamaIndex, or similar AI orchestration frameworks. Experience building enterprise AI applications with governance, security, and compliance considerations. Knowledge of MLOps/LLMOps practices, CI/CD pipelines, and model lifecycle management. Key Responsibilities
Design and develop scalable AI-powered microservices and APIs using Python and FastAPI. Build and maintain enterprise-grade RAG and Graph RAG solutions. Develop intelligent agentic workflows that leverage organizational knowledge and business context. Optimize knowledge retrieval, context orchestration, and response quality for AI applications. Implement evaluation frameworks and monitoring solutions using DeepEval and Arize. Deploy, monitor, and maintain AI applications on Azure. Collaborate with product, engineering, and business teams to translate use cases into production-ready AI solutions. Must Have Existing Hands-On Experience
Semantic Kernel LangGraph Neo4j or Graph Databases Kubernetes and Docker GitHub Actions / Azure DevOps Additional Note:
This version is targeted at a S Generative AI Engineer (7-10+ years experience).
#J-18808-Ljbffr