Ai Ml Engineer
Summary
AI/ML Tech Lead for a media organization: architect and scale multimodal AI systems across video, audio, and image pipelines — scene detection, summarization, content moderation, and multimodal search. Core stack includes CV/NLP/speech transformers, distributed training (DeepSpeed, Horovod), and MLOps tools like MLflow, Ray, KServe, and Triton.
The AI/ML Tech Lead will drive the end-to-end architecture, implementation, and scaling of multimodal AI systems within a media organization. The role involves leading teams that deliver solutions across media understanding, personalization, search, content moderation, and automation, while ensuring engineering excellence, scalability, and robustness across AI systems.
Must-Have Skills- Expert-level knowledge of Computer Vision (CV), NLP, Speech Models, and Multimodal Transformer Models
- Strong understanding of GPU optimization
- Hands-on experience with distributed training, including:
- DeepSpeed
- Horovod
- Experience with real-time / near-real-time video processing
- Hands-on experience with:
- MLflow
- Ray
- Airflow
- KServe
- Triton Inference Server
- Vector Databases
- RAG
- LangChain
- Experience with Broadcast / OTT platforms
- Experience with MAM (Media Asset Management) systems
- Experience with DAM (Digital Asset Management) workflows
- Experience with Generative Imaging, such as Stable Diffusion
- Experience with Video Generation
- 6-10 years of AI/ML experience, including leadership experience
- 5+ years of hands-on ML Engineering experience
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field
Architecture & Technical Leadership
- Architect large-scale multimodal AI systems handling video, audio, and image pipelines.
- Design scalable training, inference, and data-ingestion systems for media AI applications.
- Lead the architecture and development of video AI capabilities, including:
- Scene Detection
- Video Summarization
- Content Moderation
- Annotation Workflows
- Multimodal Search & Discovery
- Design and optimize RAG pipelines using:
- Transcripts
- Video frames
- Subtitles
- Metadata
- Drive technical decisions around AI/ML architecture, scalability, performance, and reliability.
- Lead engineering teams in delivering production-ready AI solutions.
- Ensure AI systems are robust, scalable, and optimized for real-time or near-real-time media processing.
- AI/ML Tech Lead
- Machine Learning
- Computer Vision
- NLP
- Speech Models
- Multimodal AI
- Transformers
- GPU Optimization
- Real-Time Video Processing
- Vector DB
- Stable Diffusion
- Video Generation
- OTT
- Broadcast
- MAM
- DAM