Job responsibilities
- Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
- Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
- Develop and maintain automated pipelines for model deployment, ensuring scalability, reliability, and efficiency.
- Conduct thorough evaluations of generative models (e.g., GPT-4), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications.
- Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
- Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
- Adds to team culture of diversity, equity, inclusion, and respect.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Experience in applied AI/ML engineering, with a track record of developing and deploying business critical machine learning models in production.
- Proficiency in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and Langchain/Llamaindex.
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization.
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures.
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications.
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
Preferred qualifications, capabilities, and skills
- Familiarity with the financial services industries.
- Expertise in designing and implementing AI/ML pipelines.
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies, RAG.
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs.