1574 AI Engineer (Expert)

SUMMARY:
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).

POSITION INFO:
ROLE & RESPONSIBILITIES:

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.

QUALIFICATIONS/EXPERIENCE:

Appropriate academic qualification in Computer Science, Engineering, or Statistics.
Demonstrated track record of delivering large-scale AI solutions for enterprise customers, including end-to-end
ownership of architecture and operations.
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