How You Will Make an Impact:

AI Research Development

- Design, develop, and fine-tune state-of-the-art generative AI models (e.g., GPT, Reliable Diffusion, DALL E, BERT, LLaMA, T5, etc.).
- Optimize LLMs, multimodal AI, and other generative models for performance, efficiency, and scalability.
- Stay up to date with the latest advancements in the Generative AI domain.
- Implement techniques such as transfer learning, model compression, reinforcement learning (RLHF), and adversarial training.
- Technical Leadership Team ManagementLead and mentor a team of AI engineers.
- Define and enforce best practices for AI model development, MLOps, and deployment.
- Collaborate with cross-functional teams, including product, design, data engineering, and software engineering.
- Drive AI project roadmaps and ensure timely execution while balancing research and productization.
- AI System Architecture InfrastructureDesign and implement scalable, high-performance AI architectures for real-world applications.
- Work with cloud-based AI services (AWS, GCP, Azure) and on-premise solutions to deploy AI models.
- Ensure AI models are optimized for low latency, cost efficiency, and high throughput.
- Data Engineering Model TrainingOversee data collection, augmentation,



and preprocessing pipelines for training generative AI models.
- Implement self-supervised learning, synthetic data generation, and data labeling strategies.
- Ensure AI models comply with data privacy regulations (GDPR, CCPA, etc.).
- Performance Monitoring OptimizationImplement real-time monitoring, logging, and debugging for AI models in production.
- Conduct A/B testing and user feedback loops to enhance AI performance.

What You Bring to the Team:

- 7+ years of experience in AI/ML engineering, including research, model development, and deployment.
- Strong background in deep learning frameworks such as TensorFlow, PyTorch, or JAX.
- Expertise in optimizing large-scale machine learning models for efficiency and performance.
- Experience in leading AI teams and managing end-to-end AI lifecycle development.
- Familiarity with cloud-based AI solutions (AWS, GCP, Azure) and MLOps best practices.
- Strong understanding of data privacy laws and ethical AI considerations.
- Minimum Bachelors Degree in Computer Science, AI, Machine Learning, or a related field. Master s/Ph.D. preferred.

Skills: MLops, Azure

Experience: 4.00-7.00 Years

📌 GenAI - Lead (Hyderabad)
🏢 Innovapptive
📍 Hyderabad