Note: The job is a remote job and is open to candidates in USA. Net2Source (N2S) is seeking a Senior AI Engineer to design, develop, and deploy AI-powered solutions for media and broadcast technology. The role focuses on multimodal content processing, computer vision, content recognition, content creation, marketing asset generation, and media workflow automation, while also requiring cloud engineering, system architecture, and software development expertise.
Responsibilities
- AI Engineer to design, develop, and deploy AI-powered solutions supporting content creation, marketing asset generation, and media workflow automation — with a core focus on multimodal (image, video, text) content processing, including character, content, and image recognition
Skills
- 7+ years of software engineering experience with strong SDLC and version control knowledge
- Strong proficiency in Python, with experience in Java, JavaScript, or Golang
- Hands-on Computer Vision experience is required — building or fine-tuning custom recognition/detection models (not limited to calling a managed vision API)
- Hands-on experience building and integrating LLM-based applications
- Experience with multimodal AI models across image, audio, and text — including vision-language models for open-ended content understanding (e.g., GPT-4V, Claude vision, Gemini multimodal, LLaVA, or similar)
- Experience with video processing: frame extraction, scene segmentation, and tracking entities across frames
- 5+ years AWS and 1+ year Azure experience
- Experience with scalable solutions using AWS, Azure, and Infrastructure as Code (Terraform)
- Strong understanding of digital and broadcast production workflows
- Experience with system architecture, API integrations, and performance engineering
- Strong knowledge of AI industry trends and best practices
- Experience working in Agile teams and code review environments
- Experience training/fine-tuning models to recognize proprietary or branded visual content (e.g., a studio's own characters or assets not known to public models)
- Experience with vector embeddings and similarity search (FAISS, Pinecone, pgvector) for asset/character matching
- CI/CD and DevOps practices for ML model deployment (MLOps)
- Cloud-native application development
- C4 Architecture Modeling
- Media, broadcast, or content management systems experience
Company Overview
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