Generative AI Engineer New York City, NY Hybrid In person Interview

We are seeking a highly skilled Generative AI Engineer with expertise in Large Language Models (LLMs), Python, Retrieval-Augmented Generation (RAG), and Agent Orchestration to design, build, and optimize next-generation AI solutions. In this role, you will work at the forefront of AI innovation, developing intelligent systems that enhance user experiences, automate business workflows, and deliver scalable AI-powered products.
You will collaborate closely with cross-functional teams including data scientists, software engineers, product managers, and business stakeholders to bring generative AI applications from concept to production. The ideal candidate has hands-on experience with LLMs, prompt engineering, orchestration frameworks, model evaluation, and deploying AI solutions in cloud environments.
Key Responsibilities

• Design, develop, fine-tune, and optimize large language models (LLMs) for a wide range of business and product use cases.
• Build and deploy generative AI applications using Python and AI/ML frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers.
• Develop Retrieval-Augmented Generation (RAG) pipelines by integrating vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Implement and manage agent orchestration workflows using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, or similar multi-agent systems.
• Conduct data preprocessing, feature engineering, and dataset preparation to support model training, fine-tuning, and evaluation.
• Collaborate with engineering and product teams to integrate AI models and agent-based systems into production-grade applications and APIs.
• Evaluate model and system performance using relevant metrics, and continuously improve accuracy, latency, scalability, and cost efficiency.
• Design prompt strategies, guardrails, and monitoring approaches to ensure reliable and safe LLM outputs.
• Stay current with the latest advancements in generative AI, LLM architecture, RAG, AI agents, and emerging research trends.
• Ensure compliance with ethical AI principles, security standards, and data privacy regulations throughout the AI development lifecycle.

Required Qualifications

• Proven experience in developing, fine-tuning, and deploying large language models such as GPT, BERT, T5, LLaMA, or similar architectures.
• Strong programming skills in Python with experience building AI/ML solutions in production environments.
• Hands-on experience with AI/ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
• Solid understanding of natural language processing (NLP) concepts, prompt engineering, model evaluation, and fine-tuning techniques.
• Experience designing and implementing RAG architectures, including embeddings, vector stores, document chunking, retrieval strategies, and grounding mechanisms.
• Familiarity with agent orchestration frameworks and building multi-step or multi-agent AI workflows.
• Experience with API development, microservices, and integrating AI capabilities into enterprise systems.
• Working knowledge of cloud platforms such as AWS, Google Cloud Platform, or Azure for scalable AI deployment.
• Strong analytical thinking, problem-solving ability, and effective collaboration skills.
• Bachelor s or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. Ph.D. is a plus.

Preferred Skills

• Experience deploying and managing LLM applications with MLOps/LLMOps practices, including monitoring, versioning, and experimentation.
• Familiarity with vector databases such as Pinecone, Weaviate, FAISS, Chroma, or Milvus.
• Knowledge of Docker, Kubernetes, CI/CD pipelines, and scalable deployment patterns for AI services.
• Experience with cloud-native AI services and model hosting infrastructure.
• Understanding of AI safety, model governance, observability, and responsible AI practices.
• Knowledge of additional programming languages is a plus.
• Strong publication record, research background, or contributions to open-source AI projects