Senior AI Engineer (Computer Vision) - Python, PyTorch, OpenCV - Immediate Joiner

Hiring for: An exciting InsurTech startup building an AI-native stack for the Insurance.

Role: Senior AI Engineer (Computer Vision) - Python, PyTorch, OpenCV - Immediate Joiner

Positions: 1

Experience: 4 to 6 years

Location(s): Bangalore, Mumbai

Type: On-site / Permanent

Salary: Up to INR 50 LPA (Includes 10-20% Variable)

Notice Period: Immediate to 15 days

About the role

Seeking a Senior Computer Vision Engineer to design, develop, and deploy state-of-the-art vision and AI systems for real-world applications. The ideal candidate combines deep expertise in computer vision, machine learning,and software engineering with experience delivering production-scale solutions.

Key Responsibilities

• Architect and develop computer vision and video analytics solutions.

• Build and optimize models for object detection, segmentation, tracking, OCR, pose estimation, andanomaly detection.

• Deploy AI models to cloud, edge, and embedded platforms.

• Lead technical design and code reviews.

• Drive research-to-production adoption of the latest vision technologies.

• Build maintainable and scalable ML services and APIs.

• Optimize models and inference pipelines for latency, throughput, and memory efficiency.

• Design solutions that can handle real-world edge cases and operate reliably at scale.

Required Skills

• 5+ years of experience in Computer Vision, Machine Learning, or AI Engineering.

• Expert-level proficiency in Python, PyTorch, OpenCV, and deep learning.

• Strong experience with:

-Object Detection

-Segmentation

-Multi-Object Tracking

-Video Analytics

-Image Processing

-Camera Geometry

• Experience with:

- Video analytics and temporal modelling

- Tracking at scale

• Experience deploying and maintaining production-grade computer vision systems.

• Strong understanding of real-world computer vision challenges.

• Good understanding of:

- Vision Transformers (ViTs)

- Multimodal and Vision-Language Models (VLMs)

Good to have

• Docker

• MLOps

• ONNX

• TensorRT

• NVIDIA GPUs

• AWS, Azure, or GCP

• Browser-based deployment

• Quantization techniques

• GPU profiling and performance optimization