AI Engineer (Expert)

1574 AI Engineer (Expert)

Recruiter

Imizizi

Job Ref

JHB001462/Zaman

Date posted

Tuesday, June 9, 2026

Location

Menlyn, South Africa

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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