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3 Weeks ago

Karomi - Senior Machine Learning Engineer - Data Modeling

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Kochi, Kerala, India
Information Technology
Full-Time
Karomi Technology Private Limited

Overview

Role Summary

You will own the full ML stack that turns raw dielines, PDFs, and e-commerce images into a self-learning system that reads, reasons about, and designs packaging artwork.

That Includes

  • Building data-ingestion & annotation pipelines (SVG/PDF - JSON),
  • Designing / modifying model heads on top of LayoutLM-v3, CLIP, GNNs, diffusion LoRAs,
  • Training & fine-tuning on GPUs,
  • Shipping inference APIs and evaluation dashboards.
  • You'll work day-to-day with packaging designers and a product-manager; you are the technical authority on everything deep-learning for this domain.

Key Responsibilities

Area Tasks :

  • Data & Pre-processing (- 40 %) - Write robust Python scripts to parse PDF, AI, SVG; extract text, colour separations, images, panel polygons.
  • Implement Ghostscript, Tesseract, YOLO, CLIP pipelines.
  • Automate synthetic-copy generation for ECMA dielines.
  • Maintain vocabulary YAMLs & JSON schemas.
  • Model R-&-D (- 40 %) - Modify LayoutLM-v3 heads (panel-ID, bbox-reg, colour, contrastive).
  • Build panel-encoder pre-train (mask-panel prediction).
  • Add Graph-Transformer & CLIP-retrieval heads; optional diffusion generator.
  • Run experiments, hyper-param sweeps, ablations; track KPIs (IoU, panel-F1, colour recall).

MLOps & Deployment (- 20 %) - Package training & inference into Docker/SageMaker or GCP Vertex jobs.

  • Maintain CI/CD, experiment tracking (Weights&Biases, MLflow).
  • Serve REST/GraphQL endpoints that designers and the web front-end call.
  • Implement active-learning loop that ingests designer corrections nightly.

Must-Have Qualifications

  • 5 + years Python, 3 + years deep-learning (PyTorch, Hugging Face).
  • Hands-on with Transformer-based vision-language models (e.g. LayoutLM, Pix2Struct) and at least one object-detection pipeline (YOLOv5/8, DETR).
  • Comfortable hacking PDF/SVG tool-chains: PyMuPDF/pdfplumber, Ghostscript, svgpathtools, OpenCV.
  • Experience designing custom heads / loss functions and fine-tuning large pre-trained checkpoints on limited data.
  • Solid Linux & GPU know-how; can spin up, monitor, and profile multi-GPU jobs.
  • Familiarity with graph neural networks or relational transformers.
  • Clear, idiomatic Git & code-review discipline; writes reproducible experiments.

Nice-to-Have

  • Knowledge of colour science (Lab, ICC profiles, Pantone tables) or print production.
  • Prior work on multimodal retrieval (CLIP, ImageBind) or diffusion fine-tuning (LoRA, ControlNet).
  • Packaging / CPG industry exposure (Nutrition Facts, Drug Facts, ECMA codes).
  • Experience standing up FAISS or similar vector search, and with AWS/GCP ML tooling.
  • Familiarity with Typescript/React front-ends for quick label-preview UIs.

Tool Stack You'll Own

  • Domain Primary tools
  • DL frameworks PyTorch, Hugging Face Transformers, torch-geometric
  • Parsing / CV PyMuPDF, pdfplumber, svgpathtools, OpenCV, Ghostscript
  • OCR / Detectors Tesseract, YOLOv8, Grounding DINO (optional)
  • Retrieval CLIP / ImageBind + FAISS
  • MLOps Docker, GitHub Actions, W&B or MLflow, AWS SageMaker / GCP Vertex
  • Languages 95 % Python, occasional Bash / JSON / YAML

Deliverables In The First 6 Months

  • Data pipeline v1 that converts > 500 ECMA dielines + 200 PDFs into training-ready JSON.
  • Panel-encoder checkpoint with < 5 % masked-panel error.
  • MVP copy-placement model (LayoutLM-v3 backbone + heads) hitting - 85 % IoU on validation.
  • REST inference service + designer preview UI able to draft lid/side-wrap artwork for one SKU in
  • Nightly active-learning retrain loop.

  • Reporting & Team

    • Reports to Head of AI (or CTO).
    • Collaborates with 1 front-end engineer, 1 product manager, 2 packaging-design SMEs.

    (ref:hirist.tech)
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