
C5i - Generative AI Engineer - Large Language Models
4 weeks ago
About C5i :
C5i is a pure-play AI & Analytics provider that combines the power of human perspective with AI technology to deliver trustworthy intelligence. The company drives value through a comprehensive solution set, integrating multifunctional teams that have technical and business domain expertise with a robust suite of products, solutions, and accelerators tailored for various horizontal and industry-specific use cases.
At the core, C5is focus is to deliver business impact at speed and scale by driving adoption of AI-assisted decision-making.
C5i caters to some of the worlds largest enterprises, including many Fortune 500 companies. The companys clients span Technology, Media, and Telecom (TMT), Pharma & Lifesciences, CPG, Retail, Banking, and other sectors. C5i has been recognized by leading industry analysts like Gartner and Forrester for its Analytics and AI capabilities and proprietary AI-based platforms.
Required Skills :
- 3+years of experience in machine learning, deep learning, or Al research, With a focus on generative models.
- Experience With generative models such as GANS (Generative Adversarial Networks), VAEs (Variational Autoencoders), and transformer-based models (e.g., GPT-3/4, BERT, DALL.E) RAG and Prompt Engineering.
- Understanding of model fine-tuning, transfer learning, and prompt engineering in the context Of large language models (LLMS).
- Strong programming skills in python and familiarity with relevant libraries and frameworks.
- Hands-on experience in building data applications using Streamlit or similar tools.
- Deep understanding of transformer architecture, including multi-head attention, layer normalization, and residual connections.
- Proficiency with Hugging Face Transformers, PyTorch or TensorFlow.
- Experience in training and deploying LLMs for tasks like text generation, summarization, or translation.
- Familiarity with AdamW, learning rate warm-up, and optimization strategies.
- Exposure to advanced transformer variants (e.g., Transformer-XL, Longformer, BigBird, LoRA).
- Solid debugging and performance tuning skills.
Good-to-Have :
- Experience with Docker, Kubernetes, and MLOps.
- Knowledge of CI/CD pipelines and model deployment best practices.
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