Data Scientist
Job Location:
Job Location:
Pretoria - South Africa
Monthly Salary:
Not Disclosed
Monthly Salary:
Posted on:
21 days ago
Posted on:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Reference: JHB001417-NN-1
ESSENTIAL SKILLS:
ADVANTAGEOUS SKILLS:
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:
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- 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.
- Appropriate academic qualitfication 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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Required Experience:
IC
