Ai Ml Engineer

: AI/ML Engineer Multimodal & Distributed Systems

Role Overview:

We are seeking an experienced AI/ML Engineer with deep expertise in multimodal transformer models, distributed training, and real-time video processing. The ideal candidate will be hands-on with modern MLOps tools and frameworks, capable of building scalable AI solutions for production environments.

Mandatory Skills:

• AI/ML Expertise
• Strong background in Computer Vision (CV), Natural Language Processing (NLP), and Speech models
• Experience with multimodal transformer architectures
• High-Performance Computing
• GPU optimization for large-scale deep learning workloads
• Distributed training using DeepSpeed and Horovod
• Real-Time Systems
• Proven experience in real-time / near-real-time video processing
• MLOps & Deployment
• Hands-on with MLflow, Ray, Airflow
• Model serving with KServe and Triton Inference Server
• Retrieval-Augmented Generation (RAG) & LLM Tooling
• Experience with Vector Databases (e.g., FAISS, Milvus, Pinecone)
• Familiarity with LangChain for building RAG pipelines

Good-to-Have Skills

• Experience with broadcast/OTT platforms, MAM systems, and DAM workflows
• Exposure to generative imaging (e.g., Stable Diffusion) or video generation

Responsibilities

• Design, train, and optimize multimodal AI models for CV, NLP, and speech tasks
• Implement distributed training pipelines across GPUs/nodes for large-scale models
• Develop real-time video processing solutions for production environments
• Deploy models using KServe/Triton and integrate with scalable MLOps workflows
• Build RAG-based applications leveraging vector databases and LangChain
• Collaborate with cross-functional teams to deliver AI-driven products at scale

Qualifications

• Bachelor’s/Master’s/PhD in Computer Science, AI/ML, or related field
• 4–8 years of experience in AI/ML engineering with focus on multimodal systems
• Strong programming skills in Python and deep learning frameworks (PyTorch, TensorFlow)
• Solid understanding of distributed systems and GPU computing