About Our Client:

We are partnering with a dynamic and rapidly expanding financial technology firm that is revolutionizing the way capital and risk management are optimized through innovative, data-driven solutions. Using their proprietary platform, they offer cutting-edge asset and liability management capabilities, equipping decision-makers with the tools needed to drive enhanced financial outcomes.

Role Overview:

This is an exciting opportunity for an AI Engineer to play a pivotal role in developing and implementing machine learning and deep learning models for a leading-edge company. If you're passionate about artificial intelligence and eager to contribute to the evolution of advanced systems, this role is ideal for you. You will collaborate with multidisciplinary teams to seamlessly integrate AI models into business systems while ensuring they are optimized for scalability and reliability.

Key Responsibilities:

• Design and implement advanced machine learning and deep learning algorithms.
• Work cross-functionally to integrate AI models into the company's core systems.
• Ensure that AI solutions are optimized for maximum performance and scalability.
• Continuously explore and implement the latest advancements in AI to enhance product offerings.
• Manage and prepare large datasets required for model training.
• Deploy AI solutions into production and monitor their effectiveness, continuously refining them for improved outcomes.

What We're Looking For:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field.
• Proficiency in Python and AI programming.
• Experience with cloud-based AI platforms like AWS Bedrock and Azure OpenAI.
• Strong knowledge of AI frameworks such as TensorFlow and PyTorch.
• Familiarity with large language models (e.g., GPT, Claude, Llama) and natural language processing (NLP) tools (e.g., NLTK, spaCy).
• Exposure to chatbot UI technologies like Streamlit, Gradio, Flask, or Django.

Bonus Points If You Have:

• 3+ years of experience in AI/ML engineering.
• Expertise in deploying large-scale AI systems and managing MLOps pipelines.
• Familiarity with deep reinforcement learning and distributed training.
• Experience with containerization and orchestration tools (Docker, Kubernetes).