Agentic AI Engineer (Dallas)

We are seeking a highly skilled

Agentic AI Engineer

to design, develop, and deploy autonomous AI agents and workflows within the

AWS

ecosystem. The ideal candidate will have hands-on expertise in building multi-agent AI systems, integrating LLMs (such as Gemini, GPT, or Claude), and orchestrating intelligent pipelines that leverage cloud services for scalability, observability, and security.

This role requires a deep understanding of

AI architecture, vector search, orchestration frameworks, and event-driven cloud systems . You will collaborate with data engineers, MLOps teams, and solution architects to deliver real-world AI capabilities that adapt, reason, and act autonomously.

Key Responsibilities Agentic AI & LLM Integration Design and implement

autonomous AI agents

capable of reasoning, planning, and executing workflows using LLMs (Gemini, GPT, Claude, etc.). Implement multi-agent coordination frameworks (e.g., LangChain, CrewAI, AutoGen, or Semantic Kernel). Build adaptive memory systems and contextual knowledge retrieval pipelines using

Bedrock

and

Vector Search . Integrate with external APIs and enterprise systems using secure, event-driven architectures. AWS Cloud Engineering Develop and deploy AI workloads in

AWS

leveraging: Bedrock ,

Pub/Sub ,

Cloud Run ,

Cloud Functions , and

BigQuery . ECS

for storage and

Cloud Composer (Airflow)

for orchestration. Build

containerized microservices

(Docker / Kubernetes / GKE) for scalable AI workflows. Implement CI/CD pipelines using

Cloud Build

or

GitHub Actions

for rapid iteration. Data & Intelligence Layer Architect retrieval-augmented generation (RAG) pipelines using

GCP Vector Search ,

Pinecone , or

Weaviate . Connect unstructured and structured data sources to LLMs using

Bedrock . Design prompt optimization, context management, and long-term memory storage strategies. Security, Governance, and Observability Enforce IAM, service accounts, and least-privilege policies across agent workflows. Integrate

Cloud Logging ,

Cloud Monitoring , and

Dynatrace

(if applicable) for full observability of agent actions. Implement data governance and compliance standards for AI model usage and external API calls. Innovation & Collaboration Partner with product, ML, and software teams to define use cases for agentic automation. Continuously evaluate emerging frameworks for multi-agent systems and adaptive reasoning. Contribute to architectural roadmaps, PoCs, and AI innovation initiatives within the organization.

Qualifications Bachelors or Masters degree

in Computer Science, Data Science, or related field. 5+ years of experience in cloud-based development (GCP preferred). 3+ years of experience with

LLM-based applications

(LangChain, LlamaIndex, or OpenAI APIs). Strong programming skills in

Python, Go, or Node.js . Experience with

RAG ,

vector databases , and

agent orchestration frameworks . Familiarity with

Vertex AI ,

GKE ,

Pub/Sub ,

BigQuery , and

Cloud Functions . Solid understanding of

MLOps ,

microservices , and

event-driven design .

Preferred Skills Experience with

Google Gemini API

or other advanced foundation models. Knowledge of

Autonomous AI frameworks

(e.g., AutoGPT, BabyAGI, CrewAI). Exposure to

LangGraph

or

Semantic Kernel

for graph-based agent design. Experience integrating

AI observability tools

(Weights & Biases, Arize AI, or Vertex AI Model Monitoring). Understanding of

RAG governance , compliance, and cost optimization strategies.