Computer Vision Engineer

Computer Vision Engineer
We're Hiring: Computer Vision Engineer
Location: Hubli, Karnataka
Experience: 2+ years
Domain: Computer Vision | EO & Thermal Imaging | Object Detection & Tracking | Edge AI
Freshers are not eligible. Candidates must be willing to relocate.
About this role
You will own the computer vision pipeline for real-time electro-optical systems used in defence applications.
You will work directly with live EO and thermal camera feeds and develop algorithms for detection, classification, tracking, image enhancement and camera alignment. The objective is not to demonstrate models on recorded datasets, but to make them work reliably on real hardware under changing range, motion, lighting, weather, background and thermal conditions.
Your algorithms will run on edge compute hardware and interface directly with embedded software and motion-control systems.
This is a hands-on computer vision role, not a data-science, web AI or research-only position.
What you'll be doingDetection & Tracking
Develop and optimise real-time object detection models for EO and thermal imagery
Develop robust single-object and multi-object tracking pipelines
Maintain stable tracks through motion, partial occlusion, scale changes, clutter and temporary loss of detection
Implement target acquisition, track initiation, track confirmation, loss detection and reacquisition logic
Combine detector outputs with motion models and filtering techniques for smoother and more reliable tracking
Minimise false detections and track switching under difficult backgrounds and field conditions
EO & Thermal Vision
Work directly with live visible-spectrum and thermal camera feeds
Develop preprocessing and image-enhancement algorithms for low-contrast, low-light and thermally challenging scenes
Optimise detection and tracking independently for EO and thermal imagery
Develop image registration/alignment between multiple camera channels where required
Handle differences in resolution, field of view, frame rate and imaging characteristics between sensors
Characterise vision performance across different ranges, target sizes and environmental conditions
Camera Geometry & Calibration
Implement intrinsic and extrinsic camera calibration
Develop camera-to-system alignment and boresight calibration routines
Correct lens distortion and other geometric errors
Implement coordinate transformations between image-space measurements and system coordinates
Support calibration and alignment of multiple optical sensors
Model Development & Optimisation
Train, evaluate and improve deep-learning models using field-collected datasets
Build data pipelines for dataset preparation, augmentation and validation
Analyse failure cases and perform hard-negative mining to systematically improve model performance
Optimise neural networks for real-time inference on edge compute platforms
Convert and deploy models using TensorRT, ONNX or equivalent inference frameworks
Profile and optimise inference latency, GPU utilisation, memory usage and throughput
Evaluate trade-offs between model accuracy, latency, compute requirement and power consumption
System Integration & Testing
Integrate the vision pipeline with live camera streams and embedded software
Output tracking information with sufficiently low latency for downstream real-time control
Work with embedded software, electronics and control engineers during complete system integration
Develop logging and visualisation tools for debugging vision performance
Define and measure metrics including detection probability, false-alarm rate, tracking continuity, tracking error, reacquisition performance and end-to-end latency
Participate in outdoor and field trials, analyse recorded data and improve algorithms based on actual failures
Take algorithms from workstation development through deployment and validation on production-intent hardware
Mandatory skills and experience
2+ years of hands-on computer vision or perception development, with algorithms deployed on real video rather than only academic datasets
Strong Python and working proficiency in C++
Strong working knowledge of OpenCV
Experience with PyTorch or an equivalent deep-learning framework
Practical experience with real-time object detection
Practical experience with object tracking
Understanding of tracking concepts such as Kalman filtering, association, motion models and track management
Experience training and evaluating deep-learning vision models
Experience deploying models using TensorRT, ONNX Runtime or equivalent
Understanding of camera geometry, intrinsic/extrinsic calibration and coordinate transformations
Experience processing live camera/video streams
Strong understanding of inference latency, frame rate, threading and real-time performance constraints
Ability to analyse model failures systematically rather than only retrain models with more data
Comfortable working with Linux-based development and deployment environments
Good to have
Experience working with thermal / LWIR cameras
Experience with EO/IR image processing and enhancement
Experience with NVIDIA Jetson or similar edge AI platforms
CUDA/GPU optimisation experience
Experience with long-range or small-object detection
Experience with low-light, low-contrast or cluttered-scene computer vision
Experience with optical flow, feature matching, image registration or visual motion estimation
Experience with visible-thermal image registration or multi-sensor vision
Familiarity with GStreamer or V4L2
Experience collecting, cleaning, annotating and maintaining real-world computer vision datasets
Familiarity with model quantisation, pruning or other edge-inference optimisation techniques
What we will evaluate you on
We care less about how many models you have trained and more about whether you can make computer vision work reliably outside a controlled dataset.
You should be able to explain:
Why a detector or tracker fails in a particular scene
How you would diagnose false positives and missed detections
How you would improve tracking when detections intermittently disappear
How camera motion affects tracking
How EO and thermal imagery differ from a computer vision perspective
How you would balance accuracy against latency on constrained edge hardware
How you would establish whether an improvement seen on a dataset actually improves field performance
Why join us
Work on computer vision that operates on real EO and thermal hardware, not offline demonstrations
See your algorithms progress from development datasets to integrated hardware and field trials
Own the complete perception pipeline from data and models through optimisation and deployment
Work directly with embedded software, electronics, controls and mechanical engineers
Solve computer vision problems where latency, reliability and real-world performance matter
About Astr Defence
Astr Defence is an award-winning Indian defence manufacturer developing next-generation defence systems for modern battlefield requirements.
Apply: Send your profile to career@astrdefence.com with the subject "Computer Vision Engineer".

Work arrangement
No