Computer Vision - Internship

We are looking for an AI Engineer with a deep understanding of neural networks
and other AI techniques, as well as traditional image processing techniques,
capable of reasoning from first principles. The role demands the ability to
analyze the data and problem statement, select appropriate features, model
architectures, loss functions, and evaluation metrics, and design or modify
neural networks accordingly.
The ideal candidate is not a "model-user", but a model designer and problem
solver, comfortable working with limited, noisy, or domain-specific data, and
combining classical computer vision with modern deep learning approaches.

Selected intern's day-to-day responsibilites include:

A. Problem Analysis & Model Selection:
1. Analyze problem statements, operational constraints, and available data to
determine:
2. Suitable feature representations
3. Supervised vs unsupervised vs self-supervised vs traditional image
processing approaches
4. Appropriate network architectures and learning paradigms, algorithms
5. Select and justify loss functions, regularization strategies, and
evaluation metrics based on task objectives.

B. Design and Development:
1. Design neural network architectures from scratch for:
2. Classification, detection, similarity matching, retrieval, etc.
3. Modify and adapt existing architectures (CNNs, Siamese networks,
autoencoders, transformers, etc.) for domain-specific requirements.
4. Optimize model size, latency, and accuracy for deployment constraints.

C. Classical Computer Vision and Hybrid Approaches:
1. Apply and integrate traditional image processing and vision techniques
such as:
2. Bag of Visual Words (BoVW)
3. Feature descriptors (SIFT, SURF, ORB, HOG, etc.)
4. Image similarity, matching, and retrieval
5. Develop hybrid pipelines combining classical vision and deep learning
where appropriate.

D. Training, Evaluation and Optimization:
1. Design training pipelines, data augmentation strategies, and validation
methodologies.
2. Diagnose training issues such as overfitting, underfitting, class
imbalance, and convergence problems.
3. Perform error analysis and iterate on feature sets, architectures, and
losses.

E. Collaboration and Documentation:
1. Work closely with domain experts, systems engineers, and software teams.
2. Document model choices, assumptions, and design trade-offs clearly.
3. Support integration of models into larger software or embedded systems.

What We Bring
Work on operational defense and strategic UAV systems delivering real impact.
Contribute to the next generation of autonomous aerial navigation technologies
for India's defense ecosystem.
A high-autonomy, innovation-driven environment with hands-on access to flight
systems.
Opportunity to collaborate with leading research organizations, DRDO labs, and
global technology partners.