Skit.ai is looking for a Software Engineer – Solutions in Bangalore, and they welcome applications from fresh graduates and candidates with up to 2 years of experience. This full-time position offers a competitive salary ranging from INR 10-12 LPA and is ideal for individuals with a background in Computer Science, Machine Learning, or Artificial Intelligence.
About Skit.ai
Skit.ai is a leading conversational AI platform that specializes in developing advanced machine learning models and implementing them in real-world scenarios. Working at Skit.ai means being involved in innovative projects within the fields of machine learning and computer vision, and becoming part of a skilled and cooperative team.
Job Position: Software Engineer – Solutions
The role of Software Engineer – Solutions at Skit.ai involves working on various projects related to machine learning models, object detection, and computer vision solutions. This includes collaborating with teams from diverse backgrounds to create, develop, and refine machine learning algorithms.
Job Details:
- Job Title: Software Engineer – Solutions
- Company: Skit.ai
- Location: Bangalore, India (Work from Office)
- Salary: INR 10 – 12 LPA
- Experience: 0-2 Years
- Qualification: Bachelor’s, Master’s, or PhD in Computer Science, Machine Learning, Artificial Intelligence, or related disciplines.
Key Responsibilities:
As a Software Engineer – Solutions, your main responsibilities will include:
- The key responsibilities of the Software Engineer – Solutions position include designing, building, and optimizing machine learning models focused on object detection and computer vision, as well as collaborating with technical art, software engineering, and product management teams to achieve project objectives. Additionally, it involves staying updated on the latest advancements in machine learning, researching new techniques, evaluating model performance, ensuring successful model deployment, and effectively communicating findings to both technical and non-technical audiences.
Skills Required:
To be successful in this role, you should have:
- To excel in this role, candidates should possess a strong understanding of computer vision models, hands-on expertise in machine learning using Python, TensorFlow, PyTorch, and related libraries, as well as experience with data processing libraries like Pandas and NumPy. Problem-solving abilities and effective communication and collaboration skills in cross-functional environments are also essential.
Why Should You Apply?
- Working at Skit.ai offers the opportunity to earn a competitive salary while contributing to exciting AI projects, engaging in the development of machine learning and computer vision models using the latest tools and frameworks, and benefiting from excellent career development opportunities in AI within a collaborative and inclusive work culture that values creativity and problem-solving.
How to Apply?
If you’re interested in joining Skit.ai as a Software Engineer – Solutions, follow these steps to apply:
- Click the Apply Now button on the job listing page.
- Register/Sign In: If you’re a new applicant, create an account. Existing users can log in.
- Fill in the Application Form: Provide all necessary personal, academic, and professional details.
- Upload Required Documents: Ensure your resume and any other necessary documents are uploaded.
- Submit the Application: Double-check all details and submit your application.
Top 10 Interview Questions for Skit.ai’s Software Engineer – Solutions Role
These are potential interview questions and concise responses to assist you in preparing for the Skit.ai interview:
- What attracted you to the role of Software Engineer – Solutions at Skit.ai?
- My enthusiasm for machine learning and AI, combined with Skit.ai’s emphasis on practical applications, especially in computer vision, resonates with my professional aspirations. What is your experience with machine learning libraries like TensorFlow or PyTorch?
2.What experience do you have with machine learning libraries like TensorFlow or PyTorch?
- I have extensive experience working with TensorFlow and PyTorch on academic projects, where I implemented models for tasks such as image classification and object detection. Can you elaborate on a machine learning project you have worked on?
- Can you explain a machine learning project you’ve worked on?
- I created a machine learning model for object detection using the YOLO architecture, achieving high accuracy through hyperparameter tuning and dataset optimization. How do you stay informed about the latest advancements in AI and machine learning?
- How do you stay updated with the latest advancements in AI and machine learning?
- I keep up to date with AI research papers, attend webinars, and stay abreast of industry developments through platforms like arXiv, Medium, and Kaggle. What methods do you use to assess the performance of machine learning models?
- What techniques do you use to evaluate the performance of machine learning models?
- I utilize metrics such as accuracy, precision, recall, and F1 score. For models like YOLO, I prioritize mAP (mean average precision) to evaluate object detection performance. Can you describe a situation where you had to collaborate in a cross-functional team?
- Can you describe a situation where you had to work in a cross-functional team?
- During a recent internship, I worked with software engineers and product managers to develop a computer vision solution, ensuring alignment with both technical and business objectives.
- What is your approach to solving ambiguous problems in machine learning?
- I deconstruct the problem, research similar solutions, experiment with different algorithms, and iterate based on performance feedback.
- How would you explain a complex machine learning concept to a non-technical audience?
- I would simplify the concept using real-world comparisons and emphasize the problem the model solves rather than technical terminology..
- How do you handle situations where your model underperforms?
- Collaboration ensures that technical solutions align with business goals and user requirements, resulting in more efficient and scalable products.
- Why is it important to collaborate across different functions in a project?
- Working together guarantees that technical solutions are in line with business objectives and user needs, resulting in the development of more efficient and adaptable products.
This position at Skit.ai presents an excellent chance for enthusiastic engineers who are interested in AI and machine learning. Submit your application now to become part of an innovative company and commence your career in the AI industry!