Artificial Intelligence Engineers

As a Senior Associate AI/ML Engineer, you will be responsible for building production-grade ML and multimodal systems that support various media workflows such as automated metadata extraction, video understanding, speech transcription, moderation, and recommendation. Your role will involve owning model development, MLOps integration, and production deployment while also mentoring junior engineers. **Key Responsibilities:** - Build and deploy multimodal ML models spanning NLP, CV, ASR, OCR, and video analytics. - Develop robust pipelines for: - Video frame extraction, shot detection - Speech-to-text, speaker diarization - Image tagging, content moderation - Multimodal embeddings (CLIP, SigLIP, VideoCLIP) - Implement RAG (Retrieval Augmented Generation) with multimodal indexing. - Optimize model latency for real-time content tagging or streaming workflows. - Implement batching, quantization, distillation, or GPU optimizations. - Build CI/CD pipelines for ML using GitHub Actions, Jenkins, or Azure DevOps. - Deploy models as microservices using Docker, Kubernetes, KServe, or FastAPI. - Integrate observability tools (Prometheus, Grafana) for model monitoring. - Integrate AI into CMS platforms, OTT apps, media asset management (MAM) systems. - Create APIs for search, recommendations, auto-captioning, and content summaries. - Collaborate with product, content, and media engineering teams. - Mentor associate engineers and review code. **Qualifications:** - 5 years+ ML engineering experience. - Bachelors/Masters in CS/AI. This role requires strong Python skills along with experience in PyTorch/TensorFlow. You should have expertise in NLP, CV, and speech models. Knowledge of multimodal LLM frameworks such as LangChain, LLaVA, LlamaIndex is essential. Additionally, a strong understanding of vector search tools like Pinecone, Weaviate, FAISS, and hands-on experience with Kubernetes, Docker, and CI/CD processes are necessary for this position. As a Senior Associate AI/ML Engineer, you will be responsible for building production-grade ML and multimodal systems that support various media workflows such as automated metadata extraction, video understanding, speech transcription, moderation, and recommendation. Your role will involve owning model development, MLOps integration, and production deployment while also mentoring junior engineers. **Key Responsibilities:** - Build and deploy multimodal ML models spanning NLP, CV, ASR, OCR, and video analytics. - Develop robust pipelines for: - Video frame extraction, shot detection - Speech-to-text, speaker diarization - Image tagging, content moderation - Multimodal embeddings (CLIP, SigLIP, VideoCLIP) - Implement RAG (Retrieval Augmented Generation) with multimodal indexing. - Optimize model latency for real-time content tagging or streaming workflows. - Implement batching, quantization, distillation, or GPU optimizations. - Build CI/CD pipelines for ML using GitHub Actions, Jenkins, or Azure DevOps. - Deploy models as microservices using Docker, Kubernetes, KServe, or FastAPI. - Integrate observability tools (Prometheus, Grafana) for model monitoring. - Integrate AI into CMS platforms, OTT apps, media asset management (MAM) systems. - Create APIs for search, recommendations, auto-captioning, and content summaries. - Collaborate with product, content, and media engineering teams. - Mentor associate engineers and review code. **Qualifications:** - 5 years+ ML engineering experience. - Bachelors/Masters in CS/AI. This role requires strong Python skills along with experience in PyTorch/TensorFlow. You should have expertise in NLP, CV, and speech models. Knowledge of multimodal LLM frameworks such as LangChain, LLaVA, LlamaIndex is essential. Additionally, a strong understanding of vector search tools like Pinecone, Weaviate, FAISS, and hands-on experience with Kubernetes, Docker, and CI/CD processes are necessary for this position.