Backend Engineer, ML systems

Job Summary

You'll help build the backend and ML platforms that turn multi-sensor satellite data into geospatial intelligence and you'll build them to run fully air-gapped and offline, deployed inside defense and intelligence environments with no internet access.

What you'll actually do

• Build and maintain backend services and APIs that fuse satellite data (SAR + MSI) and serve geospatial analytics to analysts.
• Design and work with databases and data flows for large raster/imagery datasets; model the data, write queries and pipelines that hold up at scale.
• Put ML models behind reliable, self-hosted services: take something that works in a notebook and make it a monitored, production service that runs offline.
• Work on inference and ML pipelines for imagery/geospatial analytics: batching, latency vs. throughput, GPU constraints, keeping things healthy in an air-gapped deployment.
• Contribute to analyst-facing and agentic tooling components that chain steps, call local tools, and support intelligence workflows (and fail gracefully when they don't).
• Debug systems where "correct" is fuzzy, and build the evals and monitoring that tell us whether a change actually made things better.

What you'll learn here

• Because this matters as much as the work:
• How to build production ML and data systems under real constraints offline, on-prem, security-first a skill very few engineers ever develop.
• How to reason about systems that are probabilistic-ally correct, not just pass/fail.
• Applied geospatial/EO machine learning and multi-sensor data fusion, mentored by [a lead with ML engineering experience / the team].
• How to stay sharp without managed services reading source code and papers, and self-hosting what others just call an API for.

Requirements

• ~1-2 years of backend engineering / ML engineering experience.
• Strong fundamentals you understand why, not just which framework method to call. You can reason about what happens between a request arriving and a response leaving.
• Systematic debugging. When something breaks, you form a hypothesis, reproduce it, and narrow it down you don't just try random fixes. (This matters double when you can't google your way out live.)
• Evidence you learn fast and on your own. You've picked up something hard recently and can explain both the thing and how you learned it.
• You surface blockers early and communicate clearly when you're stuck.
• Comfort with constraints and process. Security discipline, careful data handling, and working within an air-gapped environment are part of the job, not obstacles to route around.
• Curiosity about ML/AI with some hands-on exposure ideally you've run a model locally/self-hosted rather than only via a cloud API.

Bonus (nice to have)

• Geospatial / remote-sensing experience: GDAL, rasterio, QGIS, working with satellite imagery, SAR, or multi-spectral data.
• Self-hosting or serving ML models on-prem (e.g. Triton, ONNX Runtime, local LLMs, vLLM).
• Experience with air-gapped, on-prem, or high-security deployments; reproducible builds; offline package mirroring.
• Familiarity with containerization for isolated environments (Docker/K8s), and GPU-based inference.
• Exposure to observability, evals, or testing non-deterministic systems.
• Comfort reading a papers method section or an unfamiliar librarys source.

Job Type

Full time

Location

Bengaluru, Karnataka, India

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