Agentic AI 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.

• 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.

• 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.

• 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.