Job
Description
As an experienced Python -LLM and GenAI Engineer, you will play a crucial role in the development and optimization of cutting-edge generative AI solutions within the financial sector. Your responsibilities will include: - Prompt Engineering: Design, test, and refine advanced prompts to optimize performance for Large Language Models (LLMs) and Generative AI applications. - Model Development: Develop, train, and fine-tune deep learning models using machine learning and deep learning frameworks. - Integration: Build robust APIs and integrate LLMs and generative AI models into enterprise applications and platforms. - Python Development: Develop and optimize Python scripts and libraries to support AI model deployment and automation tasks. - Research & Development: Stay up-to-date with the latest advancements in AI and LLM technologies and apply them to enhance project outcomes. - Performance Optimization: Monitor, evaluate, and improve model accuracy, speed, and scalability. - Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to deliver high-quality solutions. In terms of required skills and experience, you should have: - Strong proficiency in Python Fast API and its AI/ML libraries (TensorFlow, PyTorch, scikit-learn, etc.). - Expertise in deep learning architectures, including transformers and large-scale language models. - Experience with prompt engineering for LLMs (e.g., GPT, BERT). - Proficiency in integrating AI models into production environments (APIs, microservices). - Familiarity with cloud platforms (AWS, Azure, GCP) and deployment tools (Docker, Kubernetes). - 3-5 years of experience in machine learning, deep learning, or natural language processing (NLP). - Proven track record of working on LLM or generative AI projects. Soft skills that would be beneficial for this role include: - Strong analytical and problem-solving skills. - Excellent communication and teamwork abilities. - Ability to work independently in a fast-paced environment. Preferred qualifications include experience with distributed training and model parallelization, knowledge of MLOps practices, exposure to fine-tuning and customizing open-source LLMs, and familiarity with vector databases (e.g., Pinecone, FAISS).,
