Data Scientist Agentic AI & MLOps (Contract) GautengHybrid ISB1501458

Our client is seeking an AI architect who designs and deploys production-grade agentic systems and expert chatbots that automate conversations and solve complex business problems at enterprise scale!

Architect sophisticated AI solutions using Amazon Bedrock AgentCore LangChain LangGraph and PyTorch on containerized Kubernetes infrastructure with advanced MLOps practices!

The ideal candidate is an industry-leading AI expert with proven track record delivering large-scale enterprise AI solutions who excels at orchestrating multi-step reasoning and agentic workflows brings deep expertise in model training/deployment with PyTorch/TensorFlow demonstrates mastery of containerization networking and performance engineering for ML workloads establishes robust MLOps/GenAI Ops practices including CI/CD and observability and provides technical leadership while mentoring teams on AI architecture and operational excellence!

Expert AI/ML engineering with agentic systems and Amazon Bedrock

Hybrid and remote working flexibility with 1960 flexible annual hours

AI architecture leadership role with MLOps and enterprise chatbots

POSITION: Contract: 01 July 2026 31 December 2028

EXPERIENCE: 8 Years related experience

COMMENCEMENT: 01 July 2026

LOCATION: Hybrid: Midrand/Menlyn/Rosslyn/Home Office rotation

TEAM: Expert AI Products / Expert Chatbots

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

Level Criteria

• Communication: Negotiate (discussions and compromise. Issues are short-term operational medium-term tactical or limited strategic nature)
• Delivery: Outcomes are complex and require the integration of several nuanced systems and processes in order to deliver on the required standards.
• Employee may be expected to provide guidance to lower level employees
• Knowledge: Industry Leader - represents the best practice leader for a product or multiple products in a region or country and will typically represent the product or service in professional boards
• Problem Solving: Create a product or system based on international best practice or guidelines from other companies that have implemented the same or similar solutions. The problems are complex and may require complex solutions in single systems
• Supervision: Can solve escalated tasks that require a deep understanding of the product or service for which there are no senior level authority to defer the task to. Can lead team leaders who each have their own team members

Essential Skills Requirements

• 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 Requirements

• 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 event-driven 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

Role Requirements

• 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 trade-offs
• 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

NB:

South African citizens/residents preferred. Valid work permit holders will be considered. By applying you consent to be added to the database and to receive updates until you unsubscribe. If you do not receive a response within 2 weeks please consider your application unsuccessful.

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