GenAI Engineer

We are looking for a **Generative AI Engineer** with 3+ years of hands-on experience in building AI-driven applications. The ideal candidate will have strong expertise in **machine learning, deep learning, and large language models (LLMs)**, with a passion for applying GenAI to solve real-world problems. You will collaborate with product, data science, and engineering teams to design, fine-tune, and deploy generative AI models at scale. ### **Key Responsibilities** - Design, fine-tune, and deploy **LLMs and other generative AI models** for production use cases. - Work with **transformer architectures** (GPT, BERT, LLaMA, etc.) for text, image, or multimodal tasks. - Build **end-to-end pipelines** for training, inference, and evaluation of AI models. - Implement **prompt engineering, RAG (Retrieval-Augmented Generation), and model optimization** for improved performance. - Integrate GenAI capabilities into **web, mobile, or enterprise applications**. - Leverage frameworks such as **LangChain, Hugging Face, TensorFlow, PyTorch**. - Collaborate with backend/frontend teams to develop **APIs and services** that serve AI models. - Ensure AI solutions meet **scalability, latency, and security** requirements. - Research and stay updated on the latest advancements in **Generative AI, LLMOps, and ML infrastructure**. ### Requirements - Bachelor’s or Master’s in Computer Science, AI/ML, Data Science, or related field. - **4+ years of experience** in machine learning, NLP, or AI development. - Proficiency in **Python** and libraries like **PyTorch, TensorFlow, Hugging Face Transformers**. - Solid understanding of **LLMs, embeddings, vector databases (Pinecone, Qdrant, Weaviate, FAISS)**. - Experience with **RAG pipelines, fine-tuning, or prompt engineering**. - Familiarity with **cloud platforms** (AWS, GCP, Azure) for ML deployment. - Strong knowledge of **APIs, microservices, and containerization (Docker, Kubernetes)**. - Experience with **LangChain / LangGraph LlamaIndex** for AI application orchestration. - Knowledge of **MLOps / LLMOps pipelines**. - Exposure to **multimodal AI** (text, image, speech). - Hands-on experience with **vector search optimizations**. - Contributions to **open-source AI projects**.