About the Role :

We are seeking a skilled Generative AI Engineer to design, build, and deploy cutting-edge AI solutions leveraging Large Language Models (LLMs), Natural Language Processing (NLP), and Generative AI frameworks.

The ideal candidate will have a strong understanding of prompt engineering, model fine-tuning, and API integrations with GenAI tools, with additional knowledge in AI/ML algorithms being a plus.

Key Responsibilities :

- Design and develop Generative AI solutions using LLMs such as GPT, Claude, Gemini, or LLaMA.

- Work with NLP pipelines for tasks such as summarization, sentiment analysis, entity extraction, and text generation.

- Implement fine-tuning, prompt optimization, and model evaluation for custom AI applications.

- Integrate and deploy AI models via APIs, SDKs, or microservices for production environments.

- Collaborate with data scientists, ML engineers, and product teams to build AI-driven applications and prototypes.

- Research and stay updated on the latest advancements in Generative AI, LLMs, and transformer architectures.

- Contribute to model performance improvements through data preprocessing, embeddings, and hyperparameter tuning.

- Document architectures, model behaviors, and experiment results for reproducibility and scalability.

Required Skills & Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.

- Hands-on experience with Generative AI tools and frameworks (OpenAI, LangChain, Hugging Face, Vertex AI, etc.).

- Strong expertise in LLMs (GPT, BERT, LLaMA, Falcon, etc.) and NLP techniques.

- Proficiency in Python and libraries like Transformers, PyTorch, TensorFlow, or spaCy.

- Experience in building prompt pipelines, RAG (Retrieval-Augmented Generation), or chatbot solutions.

- Familiarity with vector databases (FAISS, Pinecone, Chroma, etc.) for contextual data retrieval.

- Strong understanding of data preprocessing, tokenization, and embedding models.

Good to Have :

- Experience in Machine Learning and Deep Learning model development and deployment.

- Knowledge of cloud-based AI platforms (AWS Sagemaker, Azure AI, Google Cloud Vertex AI).

- Understanding of MLOps, CI/CD pipelines, and model monitoring.

- Exposure to multimodal models (text-to-image, text-to-audio, etc.).