Agentic AI Engineer – Generative AI & Agentic Systems

Location: Dallas, TX
Duration: Long-Term Contract

2 rounds of In-person interview

Work Arrangement: Onsite – 4–5 days per week in the office

Job Summary

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 hands-on experience building AI-powered applications using AWS Bedrock, Amazon Bedrock AgentCore, LangGraph, LangChain, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG) architectures.

The candidate will be responsible for developing autonomous and multi-agent systems, implementing advanced AI orchestration workflows, integrating enterprise systems, and deploying scalable, secure, and production-ready AI applications on AWS.

This role requires a strong software engineering background combined with deep expertise in Generative AI, LLMs, agentic architectures, cloud infrastructure, and enterprise application development.

Key Responsibilities

• Design, develop, and deploy autonomous and multi-agent AI systems using AWS Bedrock AgentCore capabilities.

• Build, orchestrate, and optimize AI agent workflows using LangGraph and LangChain.

• Implement agent planning, reasoning, tool calling, memory management, and decision-making capabilities.

• Design agent-to-agent communication and agent-to-system interaction patterns.

• Implement Model Context Protocol (MCP) integrations and tool-calling architectures to connect AI agents with enterprise tools and services.

• Build enterprise-scale Retrieval-Augmented Generation (RAG) solutions using AWS Bedrock foundation models and embedding models.

• Design and optimize document processing, vectorization, indexing, semantic search, retrieval, re-ranking, and response-grounding strategies.

• Develop prompt engineering frameworks, LLM evaluation methodologies, and techniques to improve AI application accuracy and reliability.

• Build conversational AI applications, enterprise knowledge assistants, AI copilots, and intelligent automation solutions.

• Integrate AWS Bedrock Guardrails and implement responsible AI practices to support security, compliance, and content safety.

• Design and develop RESTful APIs to expose AI capabilities to enterprise applications and downstream systems.

• Integrate AI agents with business applications, enterprise workflows, databases, APIs, and external systems.

• Develop intuitive front-end experiences using React and modern UI frameworks.

• Build scalable, reusable AI services and microservices following enterprise architecture standards.

• 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, reusable components, development standards, and architectural patterns for Generative AI and Agentic AI implementations.

• Collaborate with product owners, solution architects, data engineers, and business stakeholders to translate business requirements into AI-driven solutions.

• Ensure AI applications meet enterprise security, governance, compliance, scalability, and responsible AI requirements.

Required Qualifications

• 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 Retrieval-Augmented Generation (RAG) architectures.

• 2+ years of experience designing and implementing Agentic AI applications or multi-agent systems.

• Strong hands-on experience with AWS Bedrock and foundation model integrations.

• Experience with Amazon Bedrock AgentCore and agent development capabilities.

• Strong programming skills in Python and experience building production-grade AI applications.

• Hands-on experience with LangGraph and LangChain for agent orchestration and workflow development.

• Experience implementing MCP (Model Context Protocol), function calling, tool integration, and agent-to-system communication.

• Strong understanding of RAG architecture, embedding models, vector databases, semantic search, and retrieval optimization.

• Experience deploying and managing production-scale AI applications in cloud environments, preferably AWS.

• Experience designing and developing RESTful APIs and integrating AI services with enterprise applications.

• Working knowledge of React and modern front-end development frameworks.

• Experience with Terraform and Infrastructure as Code (IaC).

• Experience developing and maintaining CI/CD pipelines using GitLab.

• Strong understanding of application security, cloud security, AI governance, and responsible AI principles.

• Excellent problem-solving, communication, and collaboration skills.

Preferred Qualifications

• Experience building enterprise AI copilots, conversational assistants, and intelligent automation solutions.

• Experience implementing advanced agent orchestration and stateful workflows using LangGraph.

• Experience with AI agent evaluation, testing, observability, and benchmarking frameworks.

• Experience integrating knowledge graphs and implementing graph-based reasoning.

• Experience designing and implementing Human-in-the-Loop (HITL) workflows for AI agents.

• Knowledge of LLM performance optimization, response quality evaluation, latency reduction, and cost optimization.

• Experience implementing enterprise-grade AI governance, monitoring, and operational best practices.

• Experience designing scalable, modular, and reusable Generative AI and Agentic AI architectures.

Technical Skills

Generative AI & LLMs: Generative AI, Large Language Models (LLMs), Prompt Engineering, Foundation Models, LLM Evaluation

Agentic AI: Agentic AI, Multi-Agent Systems, Autonomous Agents, Amazon Bedrock AgentCore, Agent Orchestration, Tool Calling, Agent Memory, Planning and Reasoning

AI Frameworks: LangGraph, LangChain

RAG & Knowledge Retrieval: Retrieval-Augmented Generation (RAG), Embeddings, Vector Databases, Semantic Search, Document Indexing, Re-Ranking, Grounding, Knowledge Graphs

AWS & Cloud: AWS Bedrock, AWS Cloud Services, Foundation Model Integration, Cloud-Native AI Applications

AI Integration: Model Context Protocol (MCP), REST APIs, Enterprise Integrations, Microservices

Application Development: Python, React, Front-End Development

DevOps & Infrastructure: Terraform, Infrastructure as Code (IaC), GitLab CI/CD, Cloud Deployment

AI Security & Governance: Bedrock Guardrails, Responsible AI, AI Safety, Security, Compliance, Governance

Work Location & Engagement

• Location: Dallas, TX

• Duration: Long-Term Contract

• Work Arrangement: Onsite, 4–5 days per week

• Experience Level: Senior / Experienced Professional

Ideal Candidate Profile

The ideal candidate is a hands-on AI engineer with a strong software development foundation and proven experience delivering production-ready Generative AI and Agentic AI applications. Candidates should be comfortable designing multi-agent architectures, building RAG pipelines, orchestrating workflows with LangGraph and LangChain, integrating MCP-based tools, and deploying enterprise AI solutions using AWS Bedrock and AgentCore.

Candidates must be willing to work onsite in Dallas, TX, 4–5 days per week.