Supervised learning, gradient boosting, deep learning, representation learning, ranking and forecasting.
PYTORCH · SKLEARN01 / AI / ML Engineer · production-minded intelligence
Ride the
signal.
I build machine-learning systems that turn noisy data into dependable decisions, from feature pipelines and model experiments to APIs, monitoring and measurable product outcomes.
02 / Signal philosophy
From raw
to reliable.
I treat a model as a living signal: shaped by data quality, strengthened by disciplined evaluation, and kept useful through feedback loops long after launch.
42production experiments
18model deployments
97pipeline reliability / %
03 / Engineering stack
The signal
chain.
A practical AI/ML toolkit spanning experimentation, data systems and production inference.
Feature engineering, SQL analytics, validation layers and reproducible dataset workflows.
PYTHON · SQLExperiment tracking, model packaging, API inference, monitoring and controlled releases.
MLFLOW · DOCKEROffline metrics, error slices, calibration, drift checks and business-aligned model acceptance criteria.
EVALUATE · ITERATE04 / Featured systems
Models with
a mission.
Selected case-study directions showing how I connect model quality with product value.
Demand Current
Designed a probabilistic demand-forecasting pipeline with lag features, hierarchical evaluation and alerting for unusual forecast error.
Intent Lens
Built a text-intent classifier with embedding features, confidence thresholds and human-review routing for ambiguous cases.
Defect Pulse
Prototyped a computer-vision inspection workflow focused on recall, hard-negative mining and transparent error analysis.
Next Signal
Developed a ranking experiment that blended behavioral features with business constraints and offline-to-online validation.
05 / Experience rhythm
A loop of
learning.
The strongest ML systems come from tight feedback loops rather than one-off model launches.
Senior AI/ML Engineer · Product Intelligence
Leading model experimentation, evaluation standards and production inference improvements across high-impact product workflows.
Machine Learning Engineer · Applied Systems
Built reusable feature pipelines, trained predictive models and partnered with product teams to move experiments into measurable releases.
Data & ML Analyst · Decision Science
Turned operational datasets into forecasting, segmentation and experimentation insights while developing a foundation in statistical learning.
06 / Contact
Let's build
the next signal.
For AI/ML engineering roles, applied research, product collaborations or technical conversations, send a note.
Available for thoughtful conversations about machine learning, data products and production AI.
✉ amara@example.com
☎ +233 30 555 0192
◈ Accra, GH