Python -LLM and GenAI Engineer

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 Responsibilities:

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.

Required Skills and Experience:
Technical Skills:

• 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 Qualifications:

• Experience 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).