AI Engineer
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Job Location:
Pretoria - South Africa
Monthly Salary:
Not Disclosed
Posted on:
8 hours ago
Vacancies:
1 Vacancy
Job Summary
Reference: JHB001380-NS-1
ESSENTIAL SKILLS:
ADVANTAGEOUS SKILLS:
ESSENTIAL SKILLS:
- Deep understanding of classical and fundamental AI/ML algorithms (supervised unsupervised probabilistic
- models Bayesian methods optimization techniques).
- Strong experience designing and implementing machine learning models in production on AWS (SageMaker
- EC2 Lambda EKS).
- High proficiency in Python and key ML libraries (NumPy SciPy scikit-learn TensorFlow/PyTorch XGBoost/
- LightGBM).
- Solid software engineering skills: code quality testing CI/CD reproducible experiments and version control
- (Git).
- Proven expertise in ML model evaluation validation bias detection calibration and robustness testing.
- Extensive experience with data engineering and feature engineering: ETL data pipelines data quality and use
- of AWS data services (Glue Redshift RDS S3).
- Strong architecture skills for designing end-to-end AI solutions including model serving inference scaling
- monitoring and observability.
- Proven ability and keen willingness to mentor coach and upskill junior engineers and data scientists including
- running workshops and code reviews.
- Exceptional communication skills to translate technical concepts to stakeholders and vice verse and produce
- high quality design documentation.
- Security compliance and governance awareness for AI systems (data privacy access control model
- governance) and experience implementing controls in AWS.
ADVANTAGEOUS SKILLS:
- Solid hands-on experience with MLOps platforms and tools (SageMaker Pipelines MLflow Kubeflow TFX).
- Deep knowledge of probabilistic programming frameworks (Pyro Stan) or advanced Bayesian tooling.
- Experience with containerization and orchestration (Docker Kubernetes/EKS).
- Familiarity with deep learning for structured and unstructured data (NLP CV) even if focus remains on
- traditional methods.
- Experience with real-time or near-real-time inference architectures and streaming data (Kinesis Kafka).
- Experience deploying models with cost-optimized inference patterns (spot instances autoscaling model
- shards).
- Familiarity with Explainable AI (XAI) techniques and tools (SHAP LIME model interpretability frameworks).
- Experience in building training programs internal certifications or curriculum for upskilling technical teams.
- Certifications in AWS (e.g. AWS Certified Machine Learning - Specialty Solutions Architect) or relevant
- industry certifications.
- Knowledge of domain-specific regulations (e.g. GDPR HIPAA) and implementing privacy-preserving ML
- techniques (differential privacy federated learning).
Duties & Responsibilities
ROLE & RESPONSIBILITIES:
Submit your CV to: and Subject line Role title
- Lead the architecture design and delivery of scalable production-ready AI/ML solutions on AWS (or other tech
- stacks as per changing business requirements) with an emphasis on sound classical AI techniques.
- Define and enforce best practices for ML model development validation deployment monitoring and lifecycle
- management.
- Build and maintain end-to-end data and model pipelines using AWS native services ensuring reliability
- reproducibility and maintainability.
- Conduct model selection feature engineering and rigorous evaluation using statistical and probabilistic
- methods to ensure robust performance.
- Drive architectural decisions for model serving inference scaling rollback strategies and cost optimization.
- Implement and oversee model governance versioning monitoring drift detection and alerting for production systems.
- Mentor and upskill junior data scientists and engineers through pair programming technical reviews structure training sessions and skills roadmap coaching.
- Create and deliver internal workshops brown-bags and learning materials focused on foundational AI principle and practical implementation.
- Review and approve technical designs code and proposals from team members ensuring adherence to
- engineering standards and security controls.
- Collaborate closely with product managers stakeholders and cross-functional teams to translate business problems into well-scoped ML solutions.
- Collaborate closely with product managers stakeholders and cross-functional teams to translate business
- problems into well-scoped ML solutions.
- Lead post-implementation reviews and root cause analysis for incidents; drive continuous improvement and
- knowledge sharing.
- Advocate for ethical transparent and explainable AI; ensure models are interpretable fair and compliant with regulatory requirements.
- Willing and able to travel to global business partners as and when required.
- Any additional responsibilities assigned in the Agile Working Model Team Charter of the designated Product Sub-Product assigned to.
- Degree in Computer Science Mathematics Statistics Engineering or a related quantitative discipline;
- advanced degree (MSc/PhD) preferred.
- Minimum 10 years of hands-on experience in AI/ML engineering and architecture with at least 3 years
- deploying and operating models in AWS production environments.
- Proven track record of mentoring and developing junior team members running training programmes and
- influencing technical direction across teams.
Submit your CV to: and Subject line Role title
Required Experience:
IC
