Generative AI Engineer

Realtime Recruitment; New York (NY); Hybrid; USD 90,000 - 130,000; Full time

Job summary

Realtime Recruitment Limited is seeking an Applied Marketing AI Engineer to join an innovative team exploring next‑gen AI-powered marketing technology, with a focus on generative image and video applications. This hybrid role is based in New York or North Carolina.

You'll develop and iterate proofs of concept, build evaluation frameworks, and experiment with multimodal AI to create practical, market-ready marketing assets that align with brand identities.

Qualifications

• Hands-on experience applying LLMs and Generative AI in real-world apps.
• Strong software engineering foundations and ML systems knowledge.
• Analytical, experimental mindset with evidence-based problem solving.
• Experience with AI-powered applications, ideally with agentic or autonomous workflows.

Responsibilities

• Develop POCs for generative AI in marketing content workflows.
• Create evaluation frameworks to measure AI outputs.
• Experiment with image and video generation for brand assets.
• Bridge technical innovation with real-world customer needs.

Skills

LLMs

Agentic AI

Generative AI

Software engineering

Experimentation

Tools

LangChain

LangGraph

PostgreSQL

Redis

Docker

Kubernetes

Hugging Face

Stable Diffusion

ComfyUI

Job description

Full Time - Permanent

Hybrid / New york or North Carolina

We're looking for an Applied Marketing AI Engineer to join an innovative team exploring the next generation of AI-powered marketing technology, with a particular focus on generative image and video applications.

This is an opportunity for an experienced AI engineer who enjoys experimentation, creative problem-solving and pushing the boundaries of emerging technologies.

Working at the intersection of AI engineering, applied research and marketing technology, you'll explore how generative models and agentic AI can transform the way businesses create, manage and optimise marketing content.

The focus is on practical innovation rather than academic research. You'll have the opportunity to experiment with emerging technologies, rapidly develop prototypes and turn new ideas into practical applications with genuine commercial potential.

What You'll Be Working On

• Developing and iterating on proofs of concept (POCs) to explore new applications of generative AI across marketing and content workflows.
• Building evaluation frameworks that measure, assess and continuously improve AI-generated outputs.
• Experimenting with AI-generated imagery and video, exploring ways to improve visual quality, realism and consistency.
• Developing techniques to generate marketing and brand assets that align with specific brand identities, visual styles and guidelines.
• Exploring LLMs, multimodal models and agentic AI to solve complex marketing challenges and unlock new product capabilities.
• Taking ideas from initial experimentation through to functional prototypes, evaluating their potential for wider product adoption.

What We're Looking For

• Strong hands-on experience developing and applying LLMs and Generative AI in practical applications.
• Strong foundations in software engineering, backend development, machine learning or AI systems.
• Excellent quantitative and probabilistic thinking, with an analytical and experimental approach to problem-solving.
• Experience building, testing and evaluating AI-powered applications, ideally involving agentic systems or autonomous workflows.
• A creative and experimental mindset, with the ability to challenge conventional approaches and explore new possibilities.
• Strong product intuition and an ability to connect technical innovation with real-world customer and commercial applications.
• Experience with image generation, video generation, computer vision or multimodal AI would be particularly valuable.

Technical Experience

• LLMs & Agentic AI: OpenAI, Anthropic, Gemini, LangChain, LangGraph and LlamaIndex.
• Image & Video Generation: Diffusion models, Stable Diffusion, Hugging Face, ComfyUI and multimodal AI.
• AI Evaluation: Model evaluation, benchmarking, experimentation and automated quality assessment.
• Data & Infrastructure: RAG, embeddings, vector databases, PostgreSQL, Redis, AWS/Azure/GCP, Docker and Kubernetes.