We are seeking an experienced ML Engineer to join our team at K.I.T. The ideal candidate will have a strong background in machine learning and software development, with a passion for building innovative solutions. As an ML Engineer, you will be responsible for designing, developing, and deploying machine learning models and algorithms to drive business growth and improvement. You will work closely with cross-functional teams to identify opportunities for machine learning applications and develop solutions to complex problems. If you have a strong foundation in computer science and a passion for machine learning, we encourage you to apply for this exciting opportunity. The ML Engineer will be responsible for developing and maintaining large-scale machine learning systems, collaborating with data scientists to develop and implement new models, and working with engineers to integrate models into existing systems. You will also be responsible for staying up-to-date with industry trends and advancements in machine learning, and applying this knowledge to improve our systems and processes.

Requirement:

The requirements for this role are:

• Minimum3 to8 years of overall experience in a related field
• Experience with machine learning frameworks and tools such as TensorFlow, PyTorch, or Scikit-Learn
• Strong foundation in computer science, with a focus on algorithms, data structures, and software design
• Experience with programming languages such as Python, Java, or C++
• Job type: Payroll
• Budget: ₹2,500,000 INR per annum

Role and responsibility:

The roles and responsibilities of the ML Engineer are:

• Design, develop, and deploy machine learning models and algorithms to drive business growth and improvement
• Collaborate with cross-functional teams to identify opportunities for machine learning applications and develop solutions to complex problems
• Develop and maintain large-scale machine learning systems, including data ingestion, processing, and model deployment
• Collaborate with data scientists to develop and implement new models, and work with engineers to integrate models into existing systems
• Stay up-to-date with industry trends and advancements in machine learning, and apply this knowledge to improve our systems and processes