Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
- Work with product managers, data scientists, ML engineers, and other stakeholders to understand requirements.
- Develop api/services for model deployment, ensuring scalability, reliability, and efficiency.
- Build applications to automate manual steps in MLOPs pipeline.
- Execute POC and iterate on RAG architectures, and implement solutions for automation
- Stay informed about the latest trends and advancements in the latest LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years of applied experience.
- Proficient in programming languages like Python for model development, experimentation, and integration with OpenAI API.
- Experience is building API and restful services using Flask/Django/FastAPI
- 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.
- Knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
- Understanding of fundamentals of machine learning and LLMs.
- Proficient in identifying automation opportunities, designing and implementing solutions
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
- Experience in applied AI/ML engineering, with a track record of deploying business critical machine learning models in production.
Preferred qualifications, capabilities, and skills
- Familiarity with the financial services industries.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering.