Generative AI Engineer
Posted Updated
Summary
Generative AI Engineer at Talentport who researches the latest AI papers, builds and fine-tunes generative models (LLMs, diffusion, custom transformers), optimizes inference for production, and exposes models as APIs with RAG/vector-DB integration on GCP. Requires an MS/PhD and strong Python deep-learning research experience.
Responsibilities
- Research & Literature Survey
- Investigate and analyze the latest research papers in Generative AI, LLMs, Computer Vision, Natural Language Processing, etc.
- Monitor industry trends and assess applicability to our products
- Leverage academic presentation experience for technology evaluation from scholarly perspectives - Model Development & Implementation
- Design and develop state-of-the-art Generative AI models (GPT, Stable Diffusion, Custom Transformers, etc.)
- Customize and structurally transform existing large language models
- Implement using frameworks like PyTorch, TensorFlow, Hugging Face Transformers
- Develop models across multiple modalities (image generation, speech recognition, text generation, etc.) - Performance Optimization
- Optimize inference pipelines (reduce latency, improve throughput)
- Apply optimization techniques (quantization, distillation, pruning)
- Implement acceleration using GPU/TPU hardware
- Ensure scalability and robustness for production environments - Evaluation & Verification
- Build model performance evaluation frameworks
- Analyze and improve issues (hallucinations, bias, output quality)
- Conduct benchmark testing and drive continuous improvement cycles - Product Integration
- Implement developed models as APIs and documentation for the product team use
- Implement integration technologies (Vector DB, embeddings, RAG)
- Build and operate AI systems on cloud platforms (GCP)
Required Qualifications
- Academic & Foundational Knowledge
- Master's or Ph.D. degree in Computer Science, Software Engineering, Artificial Intelligence, Machine Learning, Mathematics, Physics, or related fields
- Deep knowledge/experience in the Generative AI domain - Technical Skills
- Advanced Python programming: Practical experience with deep learning frameworks (PyTorch, TensorFlow, Hugging Face Transformers)
- Deep learning fundamentals: Understanding of neural networks, Transformers, and attention mechanisms
- Generative AI model implementation experience: Development/implementation experience with at least one Generative AI model (LLM, diffusion models, GANs, etc.) - Research & Implementation Experience
- Design and implementation experience of algorithms based on the latest research papers in Computer Vision or Natural Language Processing
- Research project experience with independent algorithm development and publication in academic conferences/journals
- Experience applying research outcomes to actual products and achieving performance improvements - Problem-Solving Ability
- Experience analyzing specific technical challenges in real projects and proposing/implementing concrete solutions
- Ability to address complex technical problems with creative approaches