AI Engineer – Agentic AI | LLM | LangGraph | LangChain
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 LangGraph, LangChain, 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 TensorFlow, 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 LangGraph, LangChain, 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 TensorFlow, 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, AutoGen, Semantic Kernel, or similar frameworks.
- Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.
- Experience implementing RAG architectures.
- Familiarity with LangSmith, 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.
