The Role
Design and lead agentic AI system architectures using Amazon Bedrock and agent frameworks. Drive model selection, fine-tuning, containerized deployments (Docker/Kubernetes), low-latency networking, performance engineering, MLOps/CI-CD, and integrate foundation models into enterprise workflows while mentoring engineers.
Summary Generated by Built In
Define and build agentic system architectures leveraging Amazon Bedrock and agent frameworks.
Lead technical strategy for model selection, fine-tuning, and performance trade-offs.
Design and implement containerized deployment standards using Docker and Kubernetes.
Architect secure, low-latency networking for model-to-service communication.
Perform systems-level performance engineering, including load testing and capacity planning.
Establish MLOps practices, including CI/CD pipelines and model versioning.
Integrate foundation models into enterprise workflows for complex use cases.
Provide technical leadership and mentorship to engineers and stakeholders.
Requirements
ESSENTIAL SKILLS:
- System Architecture Design: Proven experience in designing and building agentic system architectures using
- frameworks like Amazon Bedrock AgentCore.
- Multi-Step Reasoning: Strong expertise in orchestrating multi-step reasoning, tool invocation, and workflow
- automation for AI agents.
- Model Training and Deployment: Deep hands-on knowledge of training and deploying models using PyTorch
- and TensorFlow.
- Containerization: Skills in Docker and Kubernetes for scalable and fault-tolerant ML/GenAI deployments.
- Networking for ML Workloads: Solid understanding of networking principles, including VPC design and lowlatency
- communication patterns.
- MLOps Practices: Experience with CI/CD for models, model versioning, and observability in ML systems.
ADVANTAGEOUS SKILLS:
- Cloud Services Experience: Prior experience with Amazon Bedrock and other cloud-managed foundation model
- services.
- Infrastructure as Code: Familiarity with tools like Terraform for reproducible cloud infrastructure.
- Serverless Architecture: Knowledge of serverless components (e.g., AWS Lambda) for event-driven workflows.
- Data Engineering: Experience in building reliable ETL/data pipelines for model training and feature stores.
- Observability Tools: Familiarity with observability stacks like Prometheus and Grafana for monitoring ML
- services.
- Enterprise Compliance: Understanding of compliance considerations in regulated industries (e.g., automotive,
- finance).
Skills Required
- Design and build agentic system architectures using frameworks like Amazon Bedrock AgentCore
- Orchestrate multi-step reasoning, tool invocation, and workflow automation for AI agents
- Train and deploy models using PyTorch and TensorFlow
- Containerization and deployment using Docker and Kubernetes
- Networking for ML workloads including VPC design and low-latency communication
- MLOps practices including CI/CD pipelines, model versioning, and observability
- Prior experience with Amazon Bedrock and other managed foundation model services
- Infrastructure as Code familiarity (e.g., Terraform)
- Serverless architecture knowledge (e.g., AWS Lambda) for event-driven workflows
- Experience building ETL/data pipelines and feature stores
- Observability stacks familiarity (Prometheus, Grafana)
- Understanding of enterprise compliance in regulated industries (automotive, finance)
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The Company
What We Do
Sabenza IT is a niche recruitment company specializing in Information Technology, SAP, Finance, and Engineering roles, with over 23 years of experience.
