Role Overview

We're seeking a Senior Fullstack Engineer whose gravitational centre is machine learning, data pipelines, and analytical systems — but who builds across the entire stack when the work demands it. You will architect ML pipelines from feature engineering through production serving, build the APIs that deliver predictions, and own the interfaces that make intelligence accessible to end users. You will work across our APEX Suite — a unified enterprise intelligence platform serving agriculture, mining, retail, banking, and international cooperatives.

At Reisiger, every engineer operates with extreme ownership: you identify the gap, propose the solution, build it, ship it, and watch it run. We are architecture-agnostic — you pick the best tool for the problem, not the familiar one. And we are AI-native — Claude Code is embedded in our daily workflow, orchestrating multi-agent tasks, accelerating development, and pushing the boundary of what a team of ~15 can deliver.

Key Responsibilities

• Design and implement end-to-end ML pipelines — data ingestion, feature engineering, model training, evaluation, and production deployment

• Build and maintain ETL/ELT pipelines that aggregate structured and unstructured data at scale across multiple client domains

• Develop NLP components for sentiment analysis, entity extraction, signal classification, and NLI-based verification

• Architect data models that serve both analytical workloads (OLAP) and transactional applications (OLTP) without compromise

• Build fullstack features end-to-end — API layer, database schema, and user-facing interfaces — when your work needs a human surface

• Own the accuracy, latency, and cost profile of ML systems in production — monitor drift, retrain models, optimise inference spend

• Design multi-tenant data processing that enforces client isolation while maximising shared infrastructure efficiency

• Orchestrate Claude Code and AI tooling to accelerate data exploration, pipeline generation, model evaluation, and documentation

• Contribute to architectural decisions across the platform — your data thinking informs how every system handles information

Required Skills & Qualifications

• 5+ years of software engineering experience with at least 3 years focused on ML/data systems in production environments

• Strong Python proficiency — your primary language for ML, data pipelines, and analytical tooling

• Hands-on experience with ML frameworks (PyTorch, scikit-learn, TensorFlow, or equivalent) beyond notebook experimentation

• Expert SQL skills — complex queries, window functions, CTEs, query plan analysis, indexing strategies

• Production ML deployment experience — model serving (SageMaker, BentoML, custom APIs), monitoring, A/B testing, retraining pipelines

• Experience building data pipelines with orchestration tooling (Airflow, Step Functions, Prefect, or custom solutions)

• Proficiency with at least one fullstack web framework (React/Next.js, Angular, or equivalent) — you ship UIs, not just models

• Working knowledge of AWS data services — S3, Glue, Athena, Comprehend, SageMaker, Lambda, RDS

• Experience with NLP techniques — embeddings, text classification, named entity recognition, sentiment analysis

• Active daily use of AI-assisted development tools — prompt engineering, code generation, agent orchestration

• Understanding of data governance, privacy requirements (POPIA), and responsible AI principles

Preferred Qualifications

• Experience with vector databases and retrieval-augmented generation (RAG) patterns — pgvector, Pinecone, or similar

• Knowledge of real-time streaming architectures — Kafka, Kinesis, or EventBridge-based event processing

• Experience with managed NLP services (AWS Comprehend or similar) for cost-effective text enrichment at scale

• Familiarity with multi-tenant ML systems — per-tenant model customisation, shared base models, entitlement-based feature gating

• Experience with infrastructure as code (Terraform, CDK) for ML infrastructure provisioning

• Background in agricultural, mining, or financial services data — understanding domain-specific data challenges

• Experience mentoring junior engineers in ML best practices and production engineering discipline

• Contributions to open-source ML or data engineering projects

• Postgraduate qualification in Computer Science, Statistics, Mathematics, or related quantitative field

Visit our website at reisiger.co.za