10 Aug
|
Recognized
|
Bengaluru
10 Aug
Recognized
Bengaluru
AI/ML Engineer (Mid)
Magnasoft · Bengaluru · Hybrid(WFO - 3Days)
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. Important 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, Kubernetes
on AWS EKS — and iterate on what production tells you.
• Work under the Principal AI
Engineer’s technical direction, and partner with the Senior Applied ML
Engineer on data quality and the eval harness.
Requirements
What we’re
looking for (must -haves)
• ~3–5 years hands -on building
production ML/AI — you’ve shipped models that real
users or customers rely on, not only POCs or coursework.
• Strong Python for both model
and product code. You write the backend and
pipeline logic around your models — post -processing, data structures, pipeline
stages, APIs — not just training scripts.
• Strong PyTorch (or
TensorFlow) and solid ML fundamentals, with the full lifecycle in your own
hands: data preparation â training â evaluation â deployment.
• MongoDB: comfortable — you can design document schemas and write non -trivial
aggregation queries.
• PostgreSQL — working knowledge; comfortable enough to be productive, with room
to deepen on the job.
• Docker and Kubernetes (we run AWS EKS), and hands -on AWS — you ship and run your own code, you don’t hand it to someone else to deploy.
• Genuinely hands -on and eager
to grow — you’ll ramp fast under a strong Principal and take on more over
time.
Our stack
Python
across the board — modeling and the AI backend / pipelines; PyTorch for modeling; a document store (MongoDB) and PostgreSQL; Docker
/ Kubernetes on AWS EKS; AWS for cloud and GPU -backed
training/inference.
Depth in ML and the Python backend/data layer
matters most — we expect on -the -job growth on the rest.
Strong
plus (any of these moves you up the stack)
• Computer vision (detection/segmentation — YOLO, Detectron2, Mask R -CNN) or OCR /
document AI.
• Geospatial / GIS exposure (imagery, GDAL/geopandas, remote sensing).
• MLOps depth — MLflow, model registry, monitoring, data/label versioning.
• RAG / GenAI / agentic exposure, or data -centric ML (annotation tooling, active
learning).
• Fluency with AI -assisted
coding (e.g., Claude Code, Copilot, Cursor) to move faster.
You might not be a fit if
• You only train models and
hand them off. This role writes the product/backend/pipeline code the
models run inside, and works daily in the data layer.
• Your background is mostly analytics
/ BI / dashboards rather than building and shipping models.
• Your ML is purely academic
or POC with nothing in production.
• You want a lead or architect
seat now — this is a hands -on, build -and -grow IC role under the Principal
(a outstanding runway, but not a leadership title on day one).
reports administratively to the VP & Head of Technology.
and the Senior Applied ML Engineer.
• Bengaluru -based. Hybrid — up to ~40% work -from -home (roughly 3 days/week in office).