Key Responsibilities: 

 AI Model Development Design and develop advanced AI/ML models, especially in generative AI, using state-of-the-art techniques and methodologies. Contribute to building scalable solutions aligned with best practices in software development.

 Model Training & Tuning Train, fine-tune, and optimize generative models to meet specific business and technical requirements, ensuring high-quality and effective results.

 Model Deployment Support Assist in deploying generative models into production, collaborating with engineers to ensure seamless integration with existing systems and workflows.

 Performance Evaluation Evaluate and benchmark the performance of generative models using appropriate testing methodologies. Continuously iterate on models for improvement.

 Documentation Create and maintain clear documentation for developed models, algorithms, and systems to ensure easy understanding and future updates by team members.

 Research & Application Keep abreast of the latest developments in generative AI and ML research. Apply emerging techniques and algorithms to current or upcoming projects.

 Collaboration Work closely with cross-functional teams (such as engineers, data scientists, and product managers) to align AI model outputs with business objectives, contributing to the successful completion of projects.

 Mentorship Provide support and guidance to junior AI engineers and data scientists. Help foster a culture of continuous learning and skill development within the team.

 Qualifications: 

 Masters degree in Computer Science, Machine Learning, or a related field (Ph.D. is a plus).

 Proven experience (5+ years) in developing AI/ML models and solutions, with a strong focus on generative AI.

 Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or Keras.

Strong programming skills in  Python and experience with data manipulation, cleaning, and preprocessing.

Experience with  cloud platforms (e.g., AWS, Azure, GCP) and deployment tools.

Ability to  independently execute projects while collaborating effectively within a team.

Practical experience in  model evaluation , including measuring accuracy, efficiency, and scalability.

Familiarity with the entire machine learning lifecycledata collection, training, deployment, and monitoring.

Ability to  work under pressure to meet deadlines while maintaining a high standard of quality.

Strong communication skills and the ability to present technical concepts to both technical and non-technical stakeholders.

Role: QA Team Manager

Industry Type: IT Services & Consulting

Department: Engineering - Software & QA

Employment Type: Full Time, Permanent

Role Category: Quality Assurance and Testing

Education

UG: B.Tech/B.E. in Any Specialization

PG: Any Postgraduate