Overview

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.

Responsibilities

• 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.
• 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
• 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
• 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.

Qualifications

• 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 have

• 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.

Education

• Bachelors degree/University degree or equivalent experience
• Masters degree preferred .