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 → correction → 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.

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 → training → evaluation → 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.

Robust 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 great runway, but not a leadership title on day one).

Team & reporting
- Works under the Principal AI Engineer technically (architecture, design, code review, mentoring); reports administratively to the VP & Head of Technology.
- One of two Mid AI/ML Engineers being hired to build the AI product core, alongside the Principal and the

Senior Applied ML Engineer.

Location & work mode

- Bengaluru-based. Hybrid — up to ~40% work-from-home (roughly 3 days/week in office).

📌 Artificial Intelligence Engineer (Bangalore Metropolitan Area)
🏢 Magnasoft
📍 Bangalore Metropolitan Area