Machine Learning Engineer

Role Summary


We are seeking highly skilled Machine Learning Engineers to design, deploy, and scale neural network powered decision engines that form the core intelligence layer of enterprise-grade platforms. You will be responsible for translating complex business problems into robust, production-ready ML systems where deep learning models serve as the foundational predictive and decision-making components.

Responsibilities

• Design, train, optimise, and deploy deep learning models as the primary decision engine for core enterprise workflows.
• Own the full lifecycle of production ML systems.
• Architect low-latency inference pipelines optimised for high-throughput transactional systems with model batching, quantisation, pruning and optimisation where applicable.
• Implement continuous model monitoring for drift, bias, and degradation. Build automated feedback loops that ingest user interactions, system outcomes, and human overrides to trigger retraining cycles and improve model adaptation over time.
• Design and enforce guardrails: confidence scoring, fallback mechanisms, human-in-the-loop approvals, audit trails, and explainability layers for regulatory and ethical compliance.
• Ensure full traceability of inputs , features , predictions , decisions, outcomes across data lineage systems.
• Integrate Large Language Models (LLMs) as augmentation layers.
• Design guardrails around LLM usage.
• Define and track domain-specific KPIs beyond accuracy.
• Communicate model behaviour, limitations, and risks clearly to technical teams, product managers, compliance officers, and C-suite stakeholders.
• Document architecture decisions, model cards, and operational playbooks for knowledge transfer and audit readiness.

Required Qualifications

• Education: Bachelor’s or Master’s in Mathematics, Computer Science, Statistics, Data Science, or a related quantitative field.
• Production Experience: Minimum 3+ years building and deploying neural network-based decision systems in enterprise environments.
• Core Technical Stack: Python. Rust, Go, C/C++ Languages for performance-critical components.