Job Title- ML Engineer – AI Suggestion & Data-Enrichment Systems Experience- 4 to 6 Years relavent experience Role: Build ML components that power an enterprise tool where users assemble item/recipe-like structures from a bespoke ERP catalogue. Your models will (1) suggest the right items and alternatives, (2) pre-populate grids/forms based on a brief, (3) learn from user overrides, and (4) 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& validation: Build reliable feature/data pipelines from ERP and external sources; add checks for missing codes, outdated cost data, and duplicates so models don’t learn from bad rows.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. Must-Have Qualifications • 4+ years in production ML (recommendation/ranking/search or adjacent). • Robust 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-data–style inputs (incomplete, late, inconsistent) and building validation/normalization layers. • Experience with MLOps (experiment tracking, model registry, CI/CD for ML, monitoring). • Able to define and track acceptance rate, top-k hit rate, coverage, freshness as product ML metrics.

📌 ML Engineer – AI Suggestion & Data-Enrichment Systems (Bengaluru)
🏢 r3 Consultant
📍 Bengaluru