AI Engineer (Expert)

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

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 low-latency 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).

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