Software Engineer Iii-gen AI Inferencing

Bank of America Bank of America · Banking · Addison, Charlotte

Software Engineer III focused on designing, building, and operating reusable toolkits for Gen AI RAG capabilities within a large enterprise. The role involves developing complex requirements, ensuring software meets functional, non-functional, and compliance needs, and maintaining architectural integrity. Experience with model serving platforms, MLOps, fine-tuning, inference frameworks, and the RAG process is required.

What you'd actually do

  1. Codes solutions and unit test to deliver a requirement/story per the defined acceptance criteria and compliance requirements
  2. Designs, develops, and modifies architecture components, application interfaces, and solution enablers while ensuring principal architecture integrity is maintained
  3. Mentors other software engineers and coach team on Continuous Integration and Continuous Development (CI-CD) practices and automating tool stack
  4. Executes story refinement, definition of requirements, and estimating work necessary to realize a story through the delivery lifecycle
  5. Performs spike/proof of concept as necessary to mitigate risk or implement new ideas

Skills

Required

  • OOP in Python/Scala/Java
  • Model Serving platform
  • MLOps
  • Fine – Tuning techniques
  • Inference Frameworks
  • generative AI RAG process
  • chunking
  • embedding
  • retrieval
  • reranking
  • summarization
  • application development
  • MongoDB
  • Redis
  • Angular/React Frameworks
  • Containerization
  • Building API based application leveraging FAST API services
  • JWT Integration
  • API Gateway
  • DevOps
  • Python development skills
  • Unix based systems

Nice to have

  • Scala
  • Java
  • Angular
  • React Frameworks
  • JWT Integration
  • API Gateway
  • enterprise devops

What the JD emphasized

  • compliance requirements
  • compliance requirements
  • generative AI RAG process

Other signals

  • design, build, and operate of reusable toolkits for Gen AI RAG capabilities
  • experience with Model Serving platform i.e. AI/ML/GenAI Lifecycle Management and Development and its Ecosystem
  • Hands on experience building frameworks using MLOps, Fine – Tuning techniques, Inference Frameworks
  • Hands on experience and knowledge generative AI RAG process for various use cases, including chunking, embedding, retrieval, reranking and summarization