Agentic AI Engineer — Multi-Agent LLM Architect
Listed on 2026-10-20
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Software Development
AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Cloud Engineer - Software, AI Reliability/ Performance Engineer
Benefits:
- ONSITE
- Competitive salary
- Opportunity for advancement
AI Engineer – Agentic AI | LLM | Lang Graph | Lang Chain
Location:
Dallas, TX (Hybrid)
Duration: 12+ Months
Interview Process:
Technical Screening + Final In-Person Interview (Mandatory)
Compensation :
Depends on Experience, Skills.
We are seeking an experienced AI Engineer with strong expertise in Agentic AI, Large Language Models (LLMs), and AI orchestration frameworks to design and develop enterprise-grade AI applications.
The ideal candidate will have hands-on experience building multi-agent systems, conversational AI solutions, and production-ready LLM applications using modern AI frameworks and cloud technologies.
This role requires a strong software engineering background with practical experience in AI orchestration, machine learning integration, prompt engineering, and LLMOps.
Responsibilities
* Design, develop, and deploy agent-based AI applications using Lang Graph, Lang Chain, and similar orchestration frameworks.
* Build scalable multi-agent workflows with intelligent task planning, execution, and state management.
* Develop reusable tools, workflows, and orchestration components for enterprise AI applications.
* Design and integrate Model Context Protocol (MCP) clients and tool ecosystems.
* Build conversational AI applications with contextual memory, reasoning, and dynamic tool invocation.
* Develop and integrate REST APIs and external enterprise systems into AI workflows.
* Integrate machine learning models using Tensor Flow, PyTorch, or Scikit-learn for inference, prediction, and feedback loops.
* Implement Retrieval-Augmented Generation (RAG), vector search, and knowledge retrieval solutions where applicable.
* Apply LLMOps best practices including prompt engineering, prompt versioning, evaluation, monitoring, logging, observability, and performance optimization.
* Define and execute testing strategies for AI applications, including unit testing, workflow validation, scenario simulation, regression testing, and agent behavior evaluation.
* Optimize AI systems for scalability, reliability, security, and cost efficiency.
* Collaborate with engineering, product, and business teams to deliver enterprise AI solutions.
* Stay current with emerging technologies, frameworks, and best practices in Agentic AI and Generative AI.
Required Qualifications
* Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
* 5+ years of software engineering experience.
* 2+ years of hands-on experience developing Generative AI or LLM-based applications.
* Strong experience with Lang Graph, Lang Chain, or similar AI orchestration frameworks.
* Experience designing and implementing multi-agent AI systems.
* Experience with Model Context Protocol (MCP) or similar tool integration architectures.
* Strong understanding of LLM architecture, prompt engineering, function calling, tool usage, memory management, and agent orchestration.
* Hands-on experience with Python.
* Experience with Tensor Flow, PyTorch, or Scikit-learn.
* Experience building REST APIs and microservices.
* Experience working with cloud platforms such as AWS, Azure, or GCP.
* Experience deploying AI applications into production environments.
* Strong problem-solving and communication skills.
Preferred Qualifications
* Experience with CrewAI, Auto Gen, Semantic Kernel, or similar frameworks.
* Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.
* Experience implementing RAG architectures.
* Familiarity with Lang Smith, Weights & Biases, Arize AI, or other LLM observability platforms.
* Experience with Docker, Kubernetes, CI/CD, and cloud-native deployments.
* Knowledge of distributed systems and scalable AI architecture.
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