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Agentic AI Engineer (Atlanta)
Agentic AI Engineer (Atlanta)Code17Tek • Atlanta, GA, US
Agentic AI Engineer (Atlanta)

Agentic AI Engineer (Atlanta)

Code17Tek • Atlanta, GA, US
[job_card.variable_days_ago]
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  • [job_card.part_time]
[job_card.job_description]

We are seeking a highly skilled Agentic AI Engineer to design, develop, and deploy autonomous AI agents and workflows within the Google Cloud Platform (GCP) 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 GCP 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 Vertex AI , BigQuery , and GCP Vector Search .
  • Integrate with external APIs and enterprise systems using secure, event-driven architectures.

GCP Cloud Engineering

  • Develop and deploy AI workloads in GCP leveraging :
  • Vertex AI , Pub / Sub , Cloud Run , Cloud Functions , and BigQuery .
  • GCS 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 Dataform , BigQuery , and Vertex AI Matching Engine .
  • 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.
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