Prompt Smart Solutions - MLOps Engineer - AI Suggestion & Data-Enrichment Systems

Role : MLops Engineer

Build ML components that power an enterprise tool where users assemble item/recipe-like structures from a bespoke ERP catalogue. Your models will

- suggest the right items and alternatives,

- pre-populate grids/forms based on a brief,

- learn from user overrides, and

- Turn unstructured inputs (notes, transcripts) into structured updates.

Everything has to work with role-based workflows and sometimes incomplete ERP data.

Responsibilities :

- AI-powered suggestions : Build ranking/recommendation models that propose the most suitable items/components based on business variables such as cost, customer/profile, category, service/class, availability, and preferred/platformed items.

- Auto-population of structures/grids : Train models to generate or pre-fill data grids/forms from a short description or template (e.g., client type, duration, constraints) using items already present in the ERP.

- Human-in-the-loop learning : Capture user actions (accept, reject, replace, mark-as preferred) and feed them back to improve future suggestions for that unit/customer/profile.

- Use of complementary data : Join ERP data with extra attributes (nutrition/impact scores, trends, satisfaction scores, custom business attributes) so ranking can optimize for more than one objective.

- Unstructured ? structured (NLP) : Take text or transcripts from review/presentation sessions and extract structured changes (replace item A with B, adjust quantity, add constraint) and map them to the right entities.

- Data pipelines & validation : Build reliable feature/data pipelines from ERP and external sources; add checks for missing codes, outdated cost data, and duplicates so models dont learn from bad rows.

- MLOps & serving : Package models behind low-latency services, register versions, enable A/B or shadow runs, and monitor latency, coverage, and suggestion quality.

Skills :

Must-Have Qualifications :

- Experience with MLOps (experiment tracking, model registry, CI/CD for ML, monitoring).

- Strong Python and ML ecosystem (pandas, scikit-learn, plus PyTorch/TensorFlow).

- Experience combining rules/business constraints with learned models in one scoring pipeline.

- Solid NLP for classification/extraction; able to work from ASR/transcripts to structured fields.

- Comfortable with ERP/master-datastyle inputs (incomplete, late, inconsistent) and building validation/normalization layers.

- Able to define and track acceptance rate, top-k hit rate, coverage, and freshness as product ML metrics.