Requirements

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  System Architecture Design: Proven experience in designing and building agentic system architectures using
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  frameworks like Amazon Bedrock AgentCore.
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  Multi-Step Reasoning: Strong expertise in orchestrating multi-step reasoning, tool invocation, and workflow
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  automation for AI agents.
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  Model Training and Deployment: Deep hands-on knowledge of training and deploying models using PyTorch
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  and TensorFlow.
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  Containerization: Skills in Docker and Kubernetes for scalable and fault-tolerant ML/GenAI deployments.
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  Networking for ML Workloads: Solid understanding of networking principles, including VPC design and lowlatency
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  communication patterns.
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  MLOps Practices: Experience with CI/CD for models, model versioning, and observability in ML systems.

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  Cloud Services Experience: Prior experience with Amazon Bedrock and other cloud-managed foundation model
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  services.
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  Infrastructure as Code: Familiarity with tools like Terraform for reproducible cloud infrastructure.
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  Serverless Architecture: Knowledge of serverless components (e.g., AWS Lambda) for event-driven workflows.
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  Data Engineering: Experience in building reliable ETL/data pipelines for model training and feature stores.
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  Observability Tools: Familiarity with observability stacks like Prometheus and Grafana for monitoring ML
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  services.
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  Enterprise Compliance: Understanding of compliance considerations in regulated industries (e.g., automotive,
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  finance).