Ai Ml Engineer

Role Overview

Senior AI/ML Engineer

This role involves building and optimizing large-scale AI systems, working closely with cross-functional engineering teams, and delivering production-grade AI solutions for video, speech, and language-based applications.

Key Responsibilities

• Design, develop, and optimize

  CV, NLP, speech, and multimodal transformer models

  for production environments.
• Implement

  GPU-accelerated training pipelines

  and optimize model performance across distributed frameworks such as

  DeepSpeed

  and

  Horovod

  .
• Build and scale

  real-time/near-real-time video processing

  pipelines for inference and analytics.
• Develop and maintain

  ML workflows

  using

  MLflow, Ray, and Airflow

  .
• Deploy scalable model-serving pipelines using

  KServe

  and

  Triton Inference Server

  .
• Implement RAG pipelines and integrate

  vector databases

  (e.g., Pinecone, Weaviate, Milvus) with LLM frameworks such as

  LangChain

  .
• Collaborate with platform teams, data scientists, and architects to deliver end-to-end AI solutions.
  
• Ensure model reproducibility, performance monitoring, and adherence to responsible AI best practices.
  

Must-Have Skills

AI/ML Modeling

• Expert in

  Computer Vision, NLP, Speech Models, and Multimodal Transformers

• Experience building

  large transformer-based architectures

  (vision-language models, speech-to-text, text-to-video, etc.)

Performance & Training

• Strong expertise in

  GPU optimization

• Knowledge of

  distributed training

  tools:

• DeepSpeed

• Horovod

Video Processing

• Hands-on experience with

  real-time / near-real-time video processing

  , encoding, and streaming pipelines

ML Engineering Tools

• MLflow

  for experiment tracking

• Ray

  for distributed workflows

• Airflow

  for orchestration

Model Serving

• Proficiency with

  KServe

• Experience with

  Triton Inference Server

LLM/RAG & Vector Stores

• Vector DBs (Pinecone, Weaviate, Milvus, Chroma)
  

• RAG pipeline development

• LangChain

  for LLM workflow orchestration

Good-to-Have Skills

• Experience working with

  broadcast/OTT platforms

  ,

  MAM (Media Asset Management)

  systems, or

  DAM (Digital Asset Management)

  workflows
• Experience with

  generative imaging

  (Stable Diffusion, ControlNet)
• Knowledge of

  video generation AI models

Qualifications

• Bachelors or Master’s degree in Computer Science, Engineering, or related field
  
• 6–12 years of experience in AI/ML engineering
  
• Proven track record of building and deploying production-grade AI systems