Roles & Responsibilities
1. Conduct original research on generative AI models, focusing on model architectures, training methods, fine-tuning, and evaluation strategies.
2. Build Proof of Concepts (POCs) with emerging AI innovations and assess their feasibility for production.
3. Design and experiment with multimodal generative models (text, images, audio, and other modalities).
4. Develop autonomous, agent-based AI systems (agentic AI) capable of adaptive decision-making.
5. Lead the design, training, fine-tuning, and deployment of generative AI systems on large datasets.
6. Optimize AI algorithms for efficiency, scalability, and computational performance using parallelization, distributed systems, and hardware acceleration.
7. Manage data preprocessing and feature engineering (cleaning, normalization, dimensionality reduction, feature selection).
8. Evaluate and validate models using industry-standard benchmarks; iterate to achieve target KPIs.
9. Provide technical leadership and mentorship to junior researchers and engineers.
10. Document research findings, model architectures, and experimental outcomes in technical reports and publications.
11. Stay updated with the latest advancements in NLP, DL, and generative AI, fostering a culture of innovation within the team.
Mandatory Technical & Functional Skills
• Strong expertise in PyTorch or TensorFlow.
• Proficiency in deep learning architectures: CNN, RNN, LSTM, Transformers, and LLMs (BERT, GPT, etc.).
• Experience fine-tuning open-source LLMs (Hugging Face, LLaMA 3.1, BLOOM, Mistral AI, etc.).
• Hands-on knowledge of PEFT techniques (LoRA, QLoRA, etc.).
• Familiarity with emerging AI frameworks & protocols (MCP, A2A, ACP, etc.).
• Deployment experience with cloud AI platforms: GCP Vertex AI, Azure AI Foundry, or AWS SageMaker.
• Proven track record in building POCs for cutting-edge AI use cases.