Artificial Intelligence Engineer

Magnasoft; Bengaluru; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

Magnasoft, based in Bengaluru, seeks a hands-on AI/ML engineer to join a small senior team.

You will build production models and the AI backend, handling data pipelines, document stores, and model evaluation, with Docker/Kubernetes on AWS EKS. This role is deeply technical and product-focused, offering growth under a Principal AI Engineer.

Qualifications

• 3–5 years hands-on building production ML/AI models.
• Strong Python for model and product code, backend and pipeline work.
• Experience with PyTorch or TensorFlow and end-to-end ML lifecycle.

Responsibilities

• Build and ship production models (object detection, OCR, classification).
• Develop AI backend and pipeline logic in Python.
• Design data structures in document store and write queries.
• Deploy and run models using Docker, Kubernetes, AWS EKS.
• Own evaluation with benchmarks and metrics tied to product outcomes.

Skills

Python

PyTorch

TensorFlow

ML model deployment

Data pipeline

Tools

MongoDB

PostgreSQL

Docker

Kubernetes

AWS

Job description

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

What you’ll do

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)

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
• 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

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)

Strong plus (any of these moves you up the stack)

• Geospatial / GIS exposure (imagery, GDAL/geopandas, remote sensing).
• 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 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

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