Ai Ml Engineer

Exp: 3 to 12 Years

Location: Bangalore, Noida, Pune, Hyderabad, Chennai, Bhubaneswar, Madurai.

Must-Have Skills

Expert in CV, NLP, speech models, and multimodal transformer models
Strong understanding of GPU optimization, distributed training (DeepSpeed, Horovod)

Experience with real-time/near-real-time video processing

Hands-on with:

MLflow, Ray, Airflow

Good-to-Have Skills

Experience with broadcast/OTT platforms, MAM systems, and DAM workflows
Experience with generative imaging (Stable Diffusion) or video generation

We are hiring skilled AI/ML Engineers with strong hands-on experience in Computer Vision, NLP, Speech Models, and Multimodal Transformer Architectures. The role involves building, training, and deploying large-scale AI models with a focus on GPU optimization, distributed training, and real-time video processing. You will work with advanced MLOps tools to develop scalable, production-ready AI pipelines.

Must-Have Skills

• Expertise in

  CV, NLP, Speech Models

  , and

  Multimodal Transformer Models

• Strong understanding of

  GPU optimization and distributed training

  frameworks (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 Pipelines

  ,

  LangChain

Responsibilities

• Design and develop AI models for CV, NLP, speech, and multimodal use cases
  
• Implement distributed training and optimize GPU utilization
  
• Build scalable AI pipelines using Ray, MLflow, and Airflow
  
• Deploy and serve models using KServe or Triton
  
• Integrate vector databases and RAG-based solutions for advanced retrieval workflows
  
• Work on real-time/near-real-time video analytics systems
  
• Ensure high performance, low latency, and production-grade stability
  

Experience Required

• 3 to 12 Years

  in AI/ML development and deployment

Preferred Qualifications

• Experience with LLMs and transformer-based models
  
• Understanding of cloud platforms (AWS/Azure/GCP)
  
• Knowledge of model optimization, quantization, or ONNX