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1458 Data Scientist

Imizizi

Job Location:

Pretoria - South Africa

Monthly Salary: Not provided by the employer

Posted: 4 June 2026 (30+ days ago)

Application Deadline: 1 September 2026

Vacancies: 1 Vacancy

The job posting is outdated and position may be filled

Job Summary

Reference: JHB001446-NS-1

ESSENTIAL SKILLS:

Proven experience designing and building agentic system architectures using Amazon Bedrock AgentCore and

agent frameworks (e.g. LangChain LangGraph Strands Agents).

Strong expertise in orchestrating multi-step reasoning tool invocation state management and workflow

automation for AI agents.

Deep hands-on knowledge of training and deploying models with PyTorch and TensorFlow.

Experience defining model strategy including architecture selection fine-tuning approaches inference

patterns and cost/performance trade-offs.

Containerization and orchestration skills: Docker and Kubernetes for scalable fault-tolerant ML/GenAI

deployments.

Solid understanding of networking for ML workloads including VPC design ingress/egress private and

internet-facing communication patterns and low-latency design.

Systems-level performance engineering: selecting CPU/GPU/accelerator hardware plus experience with load

testing stress testing and capacity planning for ML systems.

MLOps and GenAI Ops experience: CI/CD for models model versioning observability logging monitoring and incident response practices.

Strong software engineering skills in Python and familiarity with building robust production-ready APIs and back-end services.

Experience integrating foundation models with Retrieval-Augmented Generation (RAG) pipelines tool use and agentic workflows for enterprise use cases.

ADVANTAGEOUS SKILLS:

Prior experience working with Amazon Bedrock and other cloud-managed foundation model services.

Familiarity with LangChain extensions LangGraph Strands Agents or similar orchestration toolkits at scale.

Experience with infrastructure-as-code tools (e.g. Terraform Terragrunt) for reproducible cloud infrastructure.

Knowledge of serverless components (Lambda Step Functions EventBridge) for orchestration and eventdriven

workflows.

Background in secure cloud architectures IAM best practices and security hardening for AI platforms.

Experience with data engineering and building reliable ETL/data pipelines for model training and feature stores.

Familiarity with observability stacks (Prometheus Grafana CloudWatch) and distributed tracing for ML

services.

Experience optimizing inference costs through batching quantization and model distillation techniques.

Prior work with enterprise customers in regulated industries (e.g. automotive pharma finance) and

understanding of compliance considerations.

Knowledge of hybrid and multi-cloud deployment patterns for AI workloads.

Duties & Responsibilities

ROLE & RESPONSIBILITIES:

Define and build agentic system architectures that leverage Amazon Bedrock AgentCore and agent frameworks
to enable multi-step reasoning and automated workflows.
Lead technical strategy for model selection fine-tuning and inference advising on cost vs. performance tradeoffs.
Design and implement containerized deployment standards using Docker and Kubernetes to ensure consistent
scalable and fault-tolerant ML operations.
Architect secure low-latency networking for model-to-service and service-to-service communication across
private and public networks.
Perform systems-level performance engineering: select appropriate compute accelerators run load and stress
tests and conduct capacity planning for production readiness.
Establish and operate MLOps and GenAI Ops practices including CI/CD pipelines model versioning and deployment automation.
Implement observability logging monitoring and incident response for production AI systems to ensure operational excellence.
Own end-to-end system design for AI workloads: data pipelines model training inference orchestration and lifecycle management.
Integrate foundation models into enterprise RAG and tool-use pipelines enabling complex real-world use cases.
Provide technical leadership and mentorship to engineers and stakeholders on architecture best practices and operational standards.

QUALIFICATIONS/EXPERIENCE:

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

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