Senior Generative AI Engineer - Vice President

Citi•Indianapolis, IN

About The Position

Senior Generative AI Engineer - Vice President is a senior management level position responsible for accomplishing results through the management of a team or department in an effort to establish and implement new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to drive applications systems analysis and programming activities.

Requirements

Must to have 9-15 years hands-on experience as Generative AI Engineer with proven expertise of building and deploying AI agents, LLM integration, RAG pipelines, prompt engineering and the end-to-end MLOps lifecycle.
Frameworks like LangChain and AutoGen
Technical Proficiency: Strong command of Python, PyTorch, TensorFlow, and Hugging Face libraries.
GenAI Experience: Hands-on experience with LangChain, LlamaIndex, vector databases, and fine-tuning techniques (LoRA, QLoRA).
API & Backend: Proven ability to integrate AI models into web applications via APIs (OpenAI, Anthropic).
Software Engineering: Solid understanding of software engineering best practices, including Git, CI/CD, and Docker.

Nice To Haves

Experience with multimodal AI models (image, video, audio generation).
Published AI/LLM research or contributions to open-source AI projects.
Background in AI governance or safety policy development.

Responsibilities

Manage one or more Applications Development teams in an effort to accomplish established goals as well as conduct personnel duties for team (e.g. performance evaluations, hiring and disciplinary actions)
Utilize in-depth knowledge and skills across multiple Applications Development areas to provide technical oversight across systems and applications
Review and analyze proposed technical solutions for projects
Contribute to formulation of strategies for applications development and other functional areas
Develop comprehensive knowledge of how areas of business integrate to accomplish business goals
Provide evaluative judgment based on analysis of factual data in complicated and unique situations
Impact the Applications Development area through monitoring delivery of end results, participate in budget management, and handling day-to-day staff management issues, including resource management and allocation of work within the team/project
Ensure essential procedures are followed and contribute to defining standards negotiating with external parties when necessary
Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency, as well as effectively supervise the activity of others and create accountability with those who fail to maintain these standards.
AI Agent Development: Build and orchestrate AI agents using frameworks like LangChain, AutoGen, or CrewAI, implementing self-healing workflows (e.g., Act-Verify-Refine loops).
LLM Integration & Backend: Develop robust backend systems using Python and TypeScript, integrating LLMs into microservices architectures.
Data Management for LLMs: Utilize vector databases (Pinecone, Milvus, Weaviate) for agent memory and architect Retrieval-Augmented Generation (RAG) pipelines to enhance LLM accuracy and contextual understanding.
Prompt Engineering: Design and optimize prompt strategies, including automated evaluation frameworks, for high-quality LLM output.
Context Engineering: Manage LLM information ecosystems, including system prompts, RAG implementation, and conversation history.
MLOps & Deployment: Oversee the end-to-end lifecycle of generative models, focusing on inference speed, cost-efficiency, and scalability on cloud platforms (AWS, GCP, Azure).
AI Ethics & Compliance: Ensure adherence to security standards, IP regulations, and safety guidelines for all generative models.
Tool Orchestration: Define and manage the API/tool access for AI agents to optimize accuracy.