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.
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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). xsgimln
Understanding of RAG governance, compliance, and cost optimization strategies.