Roles & Responsibilities:

We are seeking a hands-on GenAI Engineer to join our team and lead the design and implementation of Generative AI-powered prototypes across Media supply chain.

• The role will focus on building out machine learning solutions for WBD’s content and Ad platform. Primary focus will be on unlocking machine learning opportunities in media supply chain, captioning services, ad conditioning, metadata extraction and building foundational machine learning training and inference pipelines at scale.

• You will lead by example and define the best practices, will set high standards for the entire team and for the rest of the organization. You have a successful track record for ambitious projects across cross-functional teams. You are passionate and results oriented. You strive for technical excellence and are very hands-on. Your co-workers love working with you. You have built respect in your career through concrete accomplishments.

• Build cutting-edge capabilities utilizing machine learning and data science (e.g., large language models, computer vision models, advanced ad & content targeting, etc.)

• Lead design and development of machine learning systems.

• Design and implement prompt-based applications using LLMs (e.g., OpenAI, Claude, Mistral). 

• Build pipelines for RAG (Retrieval-Augmented Generation) using tools like LangChain, LlamaIndex, or Haystack. 

• Integrate LLMs with external APIs, structured databases, and unstructured content (PDFs, contracts, metadata). 

• Use embedding models and vector stores (e.g., Pinecone, Weaviate, FAISS) for semantic search. 

• Develop reusable modules for key initiatives like Cairo, Rights Summarization, Deal Assist, and Contract QA automation. 

• Collaborate with TPMs, domain SMEs, and software engineers to define and implement POCs. 

• Monitor and evaluate GenAI system outputs for factual accuracy and hallucination risk. 

What to Bring:

• 5-8 years of experience designing, building AI/ML or GenAI projects.

• Strong programming skills in Python, Java, or Golang.

• Good understanding of Distributed systems, design and architecture.

• Familiarity with LLM APIs (e.g., OpenAI, Anthropic, Cohere, HuggingFace Transformers).

• Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow.

• Experience with Deep Learning, NLP, LLMs, Reinforcement Learning, Causal Inference. good knowledge of ML tools and frameworks (TensorFlow, Keras, pyTorch, scikit-learn, Spark,...).

• Exposure to RAG (Retrieval-Augmented Generation), vector databases (e.g., FAISS, Pinecone, Weaviate), and embedding techniques.

• Experience with GenAI frameworks like LangChain, LlamaIndex, or PromptLayer.

• Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring).

• Experience with offline experimentation and A/B testing.

• Understanding of batch and streaming data processing techniques.

• Knowledge of AWS or similar cloud platforms.

• Deep understanding of media supply chain workflows—including content ingestion, metadata, rights, and distribution.Masters/ Bachealors in Computer Science or related discipline.