Location: Dallas, TX
Duration: Long-Term
Work Arrangement: On-site, 4–5 days per week
Interview Process: Two rounds of face-to-face interviews
Location Requirement: Candidates must be able to work on-site in Dallas, TX.
We are seeking an experienced Agentic AI Engineer to design, develop, and deploy enterprise-grade Generative AI and Agentic AI solutions. The ideal candidate will have strong software engineering experience and hands-on expertise in building AI-powered applications using AWS Bedrock, AWS AgentCore, LangGraph, LangChain, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG) architectures.
The successful candidate will develop autonomous and multi-agent systems, build scalable AI services, integrate enterprise applications, and implement secure, production-ready AI solutions in AWS cloud environments.
Design, develop, and deploy autonomous and multi-agent AI systems using AWS AgentCore capabilities.
Build and optimize agent workflows using LangGraph and LangChain.
Implement agent planning, reasoning, tool calling, memory management, and orchestration capabilities.
Design agent-to-agent and agent-to-system interaction patterns.
Implement Model Context Protocol (MCP) integrations and tool-calling architectures.
Build enterprise-scale RAG solutions using AWS Bedrock foundation models and embedding models.
Design and optimize vectorization, indexing, retrieval, re-ranking, and response-grounding strategies.
Develop prompt engineering frameworks and evaluation methodologies for Generative AI applications.
Build conversational AI solutions, knowledge assistants, enterprise copilots, and AI-powered automation workflows.
Implement AWS Bedrock Guardrails to support AI safety, compliance, and responsible AI practices.
Design and develop RESTful APIs to expose AI capabilities to enterprise applications.
Integrate AI agents with business applications, enterprise workflows, databases, and external systems.
Develop intuitive front-end experiences using React and modern UI frameworks.
Build scalable, reusable AI services and microservices.
Deploy and manage AI solutions on AWS using Infrastructure as Code (IaC) principles.
Develop and maintain cloud infrastructure using Terraform.
Implement and maintain CI/CD pipelines using GitLab.
Establish best practices, architectural standards, and reusable patterns for Generative AI and Agentic AI implementations.
Collaborate with product owners, architects, data engineers, and business stakeholders.
Ensure AI solutions meet security, governance, compliance, and responsible AI requirements.
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related field.
7+ years of software engineering experience, including significant experience developing AI/ML solutions.
4+ years of hands-on experience with Generative AI, Large Language Models (LLMs), and RAG architectures.
2+ years of experience designing and implementing Agentic AI or multi-agent systems.
Strong hands-on experience with AWS Bedrock and AWS AgentCore.
Experience building AI agent workflows using LangGraph and LangChain.
Strong understanding of MCP, tool calling, agent orchestration, planning, reasoning, and memory management.
Experience designing and implementing enterprise-grade RAG pipelines, including vector embeddings, retrieval, re-ranking, and grounding.
Experience deploying production-scale AI applications in cloud environments.
Proficiency in Python and experience developing RESTful APIs and scalable backend services.
Experience with AWS cloud services, Terraform, and Infrastructure as Code.
Experience implementing CI/CD pipelines using GitLab.
Experience integrating AI applications with enterprise systems, databases, and external APIs.
Knowledge of AI security, governance, guardrails, and responsible AI principles.
Experience building enterprise AI copilots, conversational assistants, and knowledge-based applications.
Advanced experience designing agent orchestration workflows using LangGraph.
Experience with AI agent evaluation, testing, benchmarking, and performance optimization frameworks.
Experience integrating knowledge graphs and implementing graph-based reasoning.
Experience designing and implementing Human-in-the-Loop (HITL) workflows.
Experience developing modern user interfaces using React and related frameworks.
Experience building scalable, secure, and maintainable enterprise AI architectures.
The ideal candidate is a hands-on AI engineer who combines strong software engineering fundamentals with deep expertise in Generative AI, RAG, and autonomous agent systems. The candidate should be capable of taking AI solutions from architecture and development through deployment and production support while collaborating effectively with enterprise stakeholders.
