ML Engineer – AI Suggestion & Data-Enrichment Systems

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 4 hours ago

Vacancies: 1 Vacancy

Job Summary

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sources; add checks for missing codes outdated cost data and duplicates so models don’t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).

• 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-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.

Job Title- ML Engineer – AI Suggestion & Data-Enrichment SystemsExperience- 4 to 6 Years relavent experienceRole:Build ML components that power an enterprise tool where users assemble item/recipe-likestructures from a bespoke ERP catalogue. Your models will(1) suggest the right items and alternative...