What are the responsibilities and job description for the Generative AI Engineer position at wellsfargo?
Title: Generative AI Engineer
Location: Charlotte, NC
Work Engagement: W2
Work Schedule: Hybrid
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
Summary:
In this contingent resource assignment, you may: Consult on complex initiatives with broad impact and large-scale planning for Business Execution. Review and analyze complex multi-faceted, larger scale, or longer-term Business Execution challenges that require in-depth evaluation of multiple factors including intangibles or unprecedented factors. Contribute to the resolution of complex and multi-faceted situations requiring solid understanding of the function, policies, procedures, and compliance requirements that meet deliverables. Strategically collaborate and consult with client personnel.
Responsibilities:
Lead complex initiatives including creation, implementation, documentation, validation, articulation, and defense of highly advanced AI and Generative AI solutions.
Deliver solutions for short and long-term objectives and provide analytical support for a wide array of business initiatives.
Utilize neural network architectures, including transformer-based models such as GPT, BERT, and others for driving operational efficiencies.
Hands-on experience with fine-tuning, training, and deploying large language models (LLMs) in on-premises, cloud, or hybrid environments.
Strong hold on tokenization, embeddings, and model evaluation metrics.
Present results of analysis, solution recommendations and AI-driven strategies for variety of business initiatives.
Review and validate models and help improve the performance of the model under the preview of regulatory requirements.
Work closely with technology teams to deploy the models to production.
Qualifications:
Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
Expertise in Python and key/major frameworks like TensorFlow, PyTorch, and HuggingFace.
Proficiency in LangChain and LangGraph in building LLM powered applications using LangChain for integrating external data and tools, and LangGraph for designing stateful, workflows (leveraging GraphDB) to automate complex processes
Experience in using ML Ops tools for scaling and deploying models in production.
Experience working with on Google Cloud Platform
Experience in Scala for processing large-scale data sets for use cases.
Proficiency in Java, JavaScript (Node.js) for back-end integrations and for building interactive AI-based web applications or APIs.
Experience with SQL and NoSQL languages for managing structured, unstructured, and semi-structured data for AI and Generative AI applications.
Required to work on multiple AI and Generative AI projects and work closely with business partners across the organization.
Experience in building quick prototypes to check feasibility and value to business.
Expert in developing and maintaining modular codebase for reusability.