Generative AI Developer

Overview :

We are seeking a talented Generative AI Developer to design, develop, and deploy AI-powered solutions leveraging cutting-edge generative models (like GPT, DALL-E, Stable Diffusion, or similar). The ideal candidate will have strong experience in machine learning, NLP, computer vision, and AI model deployment, and will contribute to transforming business processes through AI innovation.

Key Responsibilities :

- Design, implement, and optimize generative AI models for NLP, vision, and multimodal tasks.

- Fine-tune, train, and evaluate pre-trained models (e.g., GPT, BERT, Transformers, diffusion models).

- Develop AI-powered applications and services using Python, PyTorch, TensorFlow, or similar frameworks.

- Integrate AI solutions into existing products or platforms using APIs, microservices, or cloud infrastructure.

- Collaborate with data engineers, software developers, and business stakeholders to translate requirements into AI solutions.

- Ensure AI models meet performance, scalability, and ethical standards.

- Conduct research on emerging AI techniques and recommend innovative solutions for business applications.

- Maintain clear documentation of AI models, pipelines, and processes.

Required Skills & Qualifications :

- 3- 7 years of experience in AI, ML, or data science roles, with hands-on generative AI experience.

- Proficiency in Python and machine learning frameworks like PyTorch, TensorFlow, Hugging Face Transformers.

- Strong understanding of NLP, computer vision, and generative modeling techniques.

- Experience with cloud platforms (AWS, Azure, GCP) and deployment of AI/ML models in production.

- Familiarity with data preprocessing, dataset creation, and model evaluation metrics.

- Strong problem-solving, analytical, and research skills.

- Excellent collaboration and communication skills for cross-functional teamwork.

Preferred Skills :

- Experience with LLMs, diffusion models, or multimodal AI systems.

- Knowledge of prompt engineering and fine-tuning strategies.

- Exposure to MLOps, model versioning, and CI/CD for AI models.

- Understanding of AI ethics, bias mitigation, and regulatory compliance.