Director of Engineering, Applied AI

A+E Networks•New York, NY

1d•$188,034 - $220,000•Hybrid

About The Position

A+E Global Media is seeking a strategic and hands-on Director of Engineering, Applied AI to lead the design, development, and delivery of AI-driven capabilities across our technology platforms. Reporting directly to the VP, Corporate Application Engineering & Enterprise Data, you will play a critical role in scaling our Applied AI and generative AI initiatives while collaborating with product, data, and business teams to bring next-generation media experiences to life. You will serve as a technical leader, coordinating with multiple teams — including Data Engineering, Application Development & Integrations — to drive innovation, ensure delivery excellence, and align AI capabilities with business goals. This is a unique opportunity to shape A+E Global Media’s Applied AI engineering function. As an Applied AI Engineering leader on the team, you’ll build and nurture a team of AI Engineers and improve the technical foundation for our Applied AI engineering team going forward.

Requirements

8 or more years of technology engineering work experience including 2+ years in Applied AI software integration experience
3 or more years of team leadership experience
Experience with Python and/or Typescript/Javascript.
Well-versed in SQL.
Experience working with LLM provider APIs (e.g., OpenAI, Anthropic, Google Gemini) and frameworks (e.g., LangChain, LlamaIndex, AutoGen, AI SDK) to build agentic or multi-agent AI workflows
Experience in building and deploying AI-powered applications at scale, with a strong focus on applying large language models (LLMs) to real-world products and workflows.
Experience working with Agentic AI, Embeddings, RAG, RLHF and other AI techniques at enterprise scale.
Experience designing or applying evals on systems built on top of LLMs, including prompt testing, grounding, hallucination detection, and performance benchmarking.
Excellent communication skills and experience working on cross-functional initiatives.

Nice To Haves

Experience with agentic AI harnesses and orchestration frameworks (e.g., LangGraph, Semantic Kernel) for building, managing, and deploying multi-agent systems is a plus
Experience working with big data and data warehouse projects is a plus
Familiarity with vector databases and RAG pipelines (e.g., Pinecone, Weaviate, Milvus, ChromaDB, pgvector) for LLM-powered applications is a plus
Experience building full-stack AI-powered applications using modern web frameworks (e.g., Next.js, React) to deliver user-facing AI products and internal tooling is a plus.
Hands-on experience with ML/deep learning libraries (e.g., PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn) and deploying models to production is a plus
Experience with MLOps tooling (e.g., MLflow, Weights & Biases, Kubeflow) and cloud ML platforms (e.g., AWS SageMaker, Google Vertex AI, Azure ML) is a plus.
Familiarity with data engineering tools and platforms (e.g., Databricks, Snowflake, Apache Spark, dbt, Kafka) is a plus
Familiarity with data privacy, copyright, and ethical issues in Applied AI applications is helpful but not needed.
Experience in the media, entertainment, ad-tech, mar-tech or publishing industry is a plus.

Responsibilities

Drive the technical roadmap for Applied AI across key enterprise applications and business workflows.
Evaluate and implement cutting-edge AI techniques, frameworks, and best practices including LLMs to ensure the team remains at the forefront of innovation and leverages the best available tools for media and entertainment use cases.
Drive development of custom AI solutions using Retrieval-Augmented Generation (RAG) pipelines and model fine-tuning to meet business-specific needs across departments, including defining evaluation criteria and benchmarks to ensure performance, grounding, and reliability.
Design and lead systems that extract value from large structured and unstructured datasets — including PDFs in S3, SharePoint documents and emails using LLMs, RAG, RLHF, and enterprise-scale modern AI techniques.
Build Agentic AI systems that streamline & automate workflows using LLMs
Cultivate a culture of experimentation, engineering rigor, and inclusiveness where bold ideas are tested quickly and learned from openly.
Oversee the full lifecycle of Agentic AI applications, from prototyping and data exploration through model integration, orchestration, deployment, monitoring, and retraining.
Partner with Data Engineering and Data Science teams to design robust, reusable data pipelines and experimentation workflows that accelerate Applied AI initiatives.
Drive AI-enabled data accessibility across the organization, delivering solutions and tooling that empower stakeholders at all levels, from analysts to senior executives to extract actionable insights.
Partner with Product and Business Analysts team to co-develop AI-enabled features and prioritize high-impact initiatives.
Work closely with Application Engineering teams to embed Agentic AI into production systems, improve user experience, ensuring seamless integration, scalability, and long-term maintainability.
Drive engineering excellence and delivery cadence across the AI stack — observability, latency, model monitoring, evaluation harnesses, rollback paths, and sprint-level prioritization across concurrent workstreams.
Build and maintain scalable, cloud-based AI infrastructure that supports rapid experimentation, model serving, and cost-controlled inference at media-scale workloads.
Champion responsible AI development aligned with privacy, IP, copyright, and ethical standards — particularly relevant for media content and audience data.