💼 Roles:
- SDE 1 – Backend → https://lnkd.in/g2_fzNPv
- Computer Vision Researcher → https://lnkd.in/giAjbx7f
🎓 Batch Eligible: 2022, 2023, 2024 Passouts
Great opportunity to work on cutting-edge AI and computer vision technologies!
Practice Verified Codes and Commands
For Backend Development (Python – Flask Example):
from flask import Flask, request, jsonify
app = Flask(<strong>name</strong>)
@app.route('/api/data', methods=['GET'])
def get_data():
return jsonify({"message": "Welcome to Spyne Backend!"})
if <strong>name</strong> == '<strong>main</strong>':
app.run(debug=True)
For Computer Vision (OpenCV Example):
import cv2
<h1>Load an image</h1>
image = cv2.imread('image.jpg')
<h1>Convert to grayscale</h1>
gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
<h1>Display the image</h1>
cv2.imshow('Grayscale Image', gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()
Linux Commands for Developers:
<h1>Check system processes</h1> ps aux | grep python <h1>Monitor system resources</h1> htop <h1>Set up a virtual environment for Python</h1> python3 -m venv myenv source myenv/bin/activate
Windows Commands for IT Professionals:
[cmd]
Check network connections
netstat -an
Display system information
systeminfo
List running services
sc query
[/cmd]
What Undercode Say
The opportunities at Spyne highlight the growing demand for backend developers and computer vision researchers in the tech industry. Backend development requires a strong understanding of frameworks like Flask or Django, as well as database management and API integration. On the other hand, computer vision researchers must be proficient in libraries like OpenCV, TensorFlow, or PyTorch to develop cutting-edge AI solutions.
For backend developers, mastering Linux commands such as ps, htop, and `grep` is essential for efficient system monitoring and debugging. Similarly, Windows commands like `netstat` and `systeminfo` are invaluable for IT professionals managing network and system resources.
In the realm of computer vision, OpenCV commands like `cv2.imread()` and `cv2.cvtColor()` are fundamental for image processing tasks. Additionally, understanding how to set up and manage Python environments using `venv` is crucial for maintaining project dependencies.
To further enhance your skills, consider exploring advanced topics like containerization with Docker, cloud computing with AWS or Azure, and continuous integration/continuous deployment (CI/CD) pipelines. These tools and practices are widely used in modern software development and can significantly boost your productivity and employability.
For more resources on backend development, visit Flask Documentation. For computer vision, check out OpenCV Tutorials.
By combining technical expertise with practical experience, you can position yourself as a strong candidate for roles like those at Spyne. Keep learning, experimenting, and building projects to stay ahead in the competitive tech landscape.
References:
Hackers Feeds, Undercode AI
