Ai Ml Engineer

Job Overview

Senior AI/ML Engineer

Key Responsibilities

• Design, build, and fine-tune

  CV, NLP, speech, and multimodal transformer models

  .
• Develop high-performance pipelines for

  real-time and near-real-time video processing

  .
• Optimize model training and inference across

  multi-GPU

  and

  distributed environments

  using DeepSpeed, Horovod, and CUDA-level improvements.
• Implement scalable and reliable ML pipelines using

  Ray, Airflow

  , and

  MLflow

  for tracking and workflow management.
• Deploy models using

  KServe

  ,

  Triton Inference Server

  , and other high-performance serving solutions.
• Build and optimize

  RAG pipelines

  , vector embeddings, and integrate vector databases using FAISS, Pinecone, Weaviate, etc.
• Use frameworks like

  LangChain

  to orchestrate multimodal and retrieval-enhanced AI systems.
• Collaborate with product, engineering, and data teams to deliver end-to-end AI features.
  
• Ensure performance, scalability, and robustness of deployed AI systems.
  

Must-Have Skills

AI/ML Expertise

• Strong proficiency in

  Computer Vision

  ,

  NLP

  ,

  Speech models

  , and

  multimodal transformers

  .
• Experience with SOTA models like CLIP, BLIP, Whisper, ViT, SAM, LLaVA, etc.
  

Performance Engineering

• Deep understanding of

  GPU optimization

  , memory management, and inference tuning.
• Hands-on experience with

  distributed training frameworks

  such as DeepSpeed, Horovod, or PyTorch Distributed.

Real-Time Processing

• Experience building

  real-time or near-real-time video analysis systems

  (streaming, processing, inferencing).

MLOps & Tools

• Hands-on experience with:
  

• MLflow

  experiment tracking, model registry

• Ray

  – distributed job execution

• Airflow

  – pipeline orchestration
• Expertise deploying models with

  KServe

  and

  Triton Inference Server

  .
• Experience with

  Vector DBs

  ,

  RAG frameworks

  , and

  LangChain

  .

Good-to-Have Skills

• Experience working with

  broadcast/OTT platforms

  ,

  Media Asset Management (MAM)

  systems, or

  Digital Asset Management (DAM)

  workflows.
• Exposure to

  generative AI for images or video

  , including

  Stable Diffusion

  or diffusion-based video generation models.
• Understanding of media processing pipelines, compression, codecs, or streaming technologies.
  

Soft Skills

• Strong problem-solving abilities and system-thinking mindset.
  
• Excellent communication for explaining complex AI architectures.
  
• Ability to collaborate effectively with cross-functional teams.
  
• Strong documentation and design articulation skills.