Engineering

Machine Learning Engineer (Production)

Abuja · Nigeria · Hybrid · Full-time

Bridge data science and platform engineering: ship reliable inference, batch pipelines, observability, and release discipline so models stay healthy after launch day.

What you will do

• Design and operate inference services (latency, autoscaling, cold start, cost) aligned with SLOs
• Build and maintain batch and streaming scoring pipelines with idempotency and backfill strategies
• Partner with ML scientists on packaging models, artifacts, and metadata for reproducible deploys
• Instrument services for logging, tracing, and metrics; define alerts tied to business impact
• Drive canary/blue-green or shadow deployments for model updates with rollback plans
• Collaborate on capacity planning, FinOps for GPU/CPU, and incident response for production AI systems
• Document runbooks, on-call expectations, and handover for client or internal operations teams

Required qualifications

• Minimum 3 years of professional software engineering experience, including serving ML or high-scale data systems in production
• Bachelor’s degree in CS, Engineering, or equivalent
• Strong Python; experience with containers (Docker) and orchestration (Kubernetes or managed equivalents)
• Understanding of REST/gRPC APIs, message queues, and at least one major cloud provider’s ML/serving stack
• Hands-on debugging of production issues (latency spikes, memory, dependency failures)
• Familiarity with CI/CD, infrastructure-as-code, and secrets management
• Comfortable reading model artifacts and collaborating on evaluation metrics with data scientists

Preferred qualifications

• Experience with feature stores, model registries, or experiment tracking at scale
• Background in SRE or platform engineering with service-level ownership
• GPU workload optimization or cost profiling experience