Job
Description
Hiring for AI ML Developer !!!! Job Description: Job Title - Senior AI Resource - Generative AI / ML Location Mumbai / Pune / Gurgaon Notice Period Immediate Joiner / 15 days max Experience 5+ Yrs 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: 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. Practical experience in model evaluation , including measuring accuracy, efficiency, and scalability. Familiarity with the entire machine learning lifecycle: data collection, training, deployment, and monitoring.
