Job description
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
System Architecture Design: Proven experience in designing and building agentic system architectures using frameworks like Amazon Bedrock Agent. Core. 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 Py.
Torch and Tensor. Flow. 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).