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 rapid 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

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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