01Overview
We are seeking an experienced Python -LLM and GenAI Engineer to join our team and play a pivotal role in developing and optimizing cutting-edge generative AI solutions in financial sector. This position involves working on deep learning models, designing effective prompts, and integrating AI solutions into various systems.Key ResponsibilitiesPrompt 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.Technical SkillsRequired Skills and Experience: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). Experience: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:Strong analytical and problem-solving skills.Excellent communication and teamwork abilities.Ability to work independently in a fast-paced environment.Preferred QualificationsExperience with distributed training and model parallelization.Knowledge of MLOps practices.Exposure to fine-tuning and customizing open-source LLMs.Familiarity with vector databases (e.g., Pinecone, FAISS). .
