BNY is looking to leverage cutting-edge AI and breakthrough technologies, specifically LLM-based AI models with RAG/Autogen and prompt engineering, to drive transformative solutions, redefine industries, and uplift communities worldwide. The goal is to unlock the full potential of AI to accurately extract data, automate tasks, and gain deeper insights from data, ultimately enhancing client offerings and integrating with external partners.
Requirements
- 5-8 years of related software coding experience
- Strong hands-on experience in Python programming, service-oriented architecture, Kubernetes, and cloud deployments.
- 1+ years of demonstrated experience building LLM and GenAI tools, including LangChain, RAG, fine-tuning, and prompt engineering.
- Proficient in developing Machine learning or LLM based AI solutions (RAG or Autogen)
- Demonstrated experience in building software solutions following Agile, test-driven development practises.
- Proficient in scripting and SQL for data management, strong understanding of SDLC lifecycle and manging cloud deployments of containers.
- Strong understanding of AI agent architecture, solid grounding and prompt engineering.
Responsibilities
- Design, develop and test LLM based AI models with RAG/Autogen and prompt engineering
- Analyse the model output to define, create, and maintain meaningful insights to support advanced analytics.
- Develop regression, stress test and automated testing frameworks for LLM models testing.
- Collaborate cross-functionally to embed RAG- enabled LLM's in product features.
- Deploy container-based services to cloud and support end to end lifecycle.
- Establish relationships to obtain data and subject knowledge necessary for analytics initiatives.
- Conduct studies to provide additional facts for informed decision-making regarding organizational and functional effectiveness.
Other
- Master’s degree in STEM or equivalent work experience.
- experience in financial services is a plus.
- Passing for solving complex data problems and developing cross functional solutions in a fast-paced environment
- Provide training in technical tools, skills, specific applications, and their business functions to maximize team contribution.
- Grow and develop skills across the three domain specialties: model science, feature science, and insight science capabilities.
