Hands-on model building and the AI backend + pipelines
Why this role exists
Magnasoft is twenty years into building one of the world’s deepest
geospatial data assets — and is now turning that asset into AI-powered software
products. Our products turn complex real-world documents and imagery into
structured, usable data using computer vision, OCR, and a human-in-the-loop
review loop.
We’re building the small, senior AI team that builds these products.
This is one of two core hands-on AI/ML engineer seats, working directly
under our Principal AI Engineer. Key to be clear up front: this is not a train-a-model-and-hand-it-off role. You build the models and the product code they live in — the AI backend, the post-processing and
pipeline logic, and the data layer. If not the AI team, no one writes that
code. Expect your time to split roughly half model work, half
backend/pipeline work.
What you’ll do
• Build and ship production
models — object detection, segmentation, OCR/text
extraction,
and classification models behind our products. Not notebooks that
die in a repo: models real customers depend on.
• Build the AI backend the
models live in. Run the models on incoming data,
then write the post-processing and pipeline logic that turns raw model
output into clean, structured product data. All in Python.
• Work in the data layer. Detected and human-corrected results are stored in a document
store (MongoDB) — you design document structures and write the queries and
aggregations your pipeline and the retraining loop depend on.
• Feed the data flywheel — the annotation â correction â retraining loop that makes the
models better release over release.
• Own evaluation for your work — benchmarks, error analysis, and quality metrics tied to real
product outcomes (cost-of-error, reviewer effort saved), not just headline
accuracy.
• Deploy and run your models and your pipeline code — Docker, Kubernet
📌 AIML Engineer (Bengaluru)
🏢 Magnasoft Consulting India
📍 Bengaluru
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