About the Role:

Green Aero Propulsion is building India's first indigenous transonic unmanned strike platform, AgniPankh. We are hiring a Computer Vision Engineer to own the EO/IR-based terminal guidance pipeline — from sensor fusion and target acquisition to real-time tracking and precision strike handoff. You will design, train, and deploy deep-learning models that recognise and lock onto targets in GPS-denied, high-speed, low-altitude flight regimes where latency and robustness are non-negotiable.

Location: KIADB Aerospace Park, Devanahalli, Bengaluru (on-site)

Employment type: Full-time

Reports to: Head of Avionics

Key Responsibilities:

- Develop and optimise real-time object detection, classification, and tracking algorithms for EO/IR sensor payloads operating at transonic speeds
- Design the terminal-guidance computer-vision pipeline: target acquisition, lock-on, mid-course update, and handoff to the flight controller for precision terminal manoeuvre
- Build, curate, and augment training datasets from UAV-captured aerial imagery, satellite imagery, thermal imagery, and synthetic data (domain randomisation, sensor-noise injection, clutter generation, weather/lighting variation)
- Train and compress deep-learning models (YOLO variants, transformer-based detectors, Siamese trackers) for edge deployment on embedded GPU/FPGA hardware under strict size, weight, and power (SWaP) constraints
- Integrate vision algorithms with the TERCOM-based GPS-denied navigation stack and the 6-DOF digital-twin simulation environment for hardware-in-the-loop (HIL) testing
- Implement sensor-fusion techniques combining EO, IR, and INS data to maintain target track through clutter, countermeasures, and environmental obscurants
- Define and run model-validation campaigns:



precision/recall benchmarks, latency profiling, Monte Carlo miss-distance and CEP analysis, and flight-test correlation
- Collaborate with the avionics, aerodynamics, and propulsion teams to ensure vision-system requirements align with platform-level performance budgets
- Stay current with advances in vision-based guidance, scene matching, and adversarial robustness; evaluate applicability to defence-grade systems

Must-Have Qualifications:

- B.Tech / M.Tech / Ph.D. in Computer Science, Electrical Engineering, Aerospace Engineering, or a related discipline with a strong computer-vision focus
- 1+ years of hands-on experience in object detection, recognition, and tracking using deep-learning frameworks (PyTorch, TensorFlow, or equivalent)
- Demonstrated work on aerial/UAV imagery — familiarity with oblique viewing angles, scale variation, motion blur, and low-contrast IR scenes
- Strong programming skills in Python and C/C++; comfort with Linux, Git, Docker, and CI/CD pipelines
- Solid understanding of image-processing fundamentals: camera models, lens distortion, homographies, multi-spectral image registration
- Familiarity with sensor-fusion concepts (EO + IR + INS) and Kalman/extended-Kalman or particle-filter-based tracking
- Working knowledge of ROS/ROS 2 for perception-pipeline integration in robotic or UAV systems




- Indian citizen with ability to obtain necessary security clearances for defence projects

Preferred Qualifications:

- Prior experience in defence, missile-guidance, or loitering-munition vision systems
- Hands-on work with scene-matching algorithms (TERCOM, DSMAC, or similar correlation-based guidance techniques)
- Experience with synthetic-aperture or millimetre-wave radar image processing
- Published research or patents in aerial object detection, visual SLAM, or adversarial robustness for safety-critical vision systems
- Experience training and deploying models on embedded edge platforms (NVIDIA Jetson, Intel Movidius, Xilinx/AMD FPGA, or comparable)
- Proficiency in model optimisation: quantisation (INT8/FP16), pruning, knowledge distillation, and TensorRT / ONNX Runtime deployment
- Familiarity with MIL-STD or DO-178C/DO-254 certification processes for airborne software/hardware
- Experience with GAN-based or diffusion-model-based synthetic data generation for rare-target augmentation
- Proficiency with simulation environments such as AirSim, Gazebo, or FlightGear for vision-in-the-loop testing

What we offer:

- A founding-team-level role in a DPIIT-recognised defence aerospace startup building category-defining indigenous platforms
- Direct ownership of the terminal-guidance vision stack from research through flight test
- Market-competitive salary with ESOP participation
- Access to in-house engine test cells, avionics labs, and a 6-DOF digital-twin simulation facility at the HiTech Defence & Aerospace Park, Bengaluru
- Opportunity to work at the intersection of AI and national security on systems that will see operational deployment
- A fast-moving, engineering-first culture where your code flies

📌 Computer Vision Engineer — Terminal Guidance (EO/IR) (Bengaluru)
🏢 Green Aero Propulsion
📍 Bengaluru