AI/ML Engineer

Role Overview

intelligent systems

This role requires strong technical depth, independent ownership, and the ability to collaborate with cross-functional teams across engineering, IT, and business domains.

Key Responsibilities

• Lead the design, development, and deployment of AI/ML models across vision, language, and prediction use cases
• Develop and implement solutions using

  LLMs

  (e.g., LLaMA, Azure OpenAI) and

  Computer Vision

  frameworks (OpenCV, PyTorch, YOLO, etc.)
• Architect

  hybrid deployment models

  using

  Azure cloud

  and

  on-premises infrastructure

  , ensuring high availability and scalability
• Collaborate with internal stakeholders using

  design thinking

  to convert business needs into AI solutions
• Develop end-to-end ML pipelines (ETL, training, evaluation, inference, monitoring)
• Optimize performance of models for real-time inference and resource efficiency (GPU/CPU)
• Ensure best practices in

  CI/CD, containerization (Docker/Kubernetes), and model lifecycle management

• Support internal teams with documentation, training, and handover of AI tools and platforms

Required Qualifications & Experience

• B.Tech from a premier engineering institute

  ;

  M.Tech preferred

• 5+ years of relevant experience in AI/ML engineering and production deployments
• Proficiency in

  Python

  ,

  PyTorch

  , and deep learning tools
• Strong experience with

  LLMs

  (e.g., LLaMA, GPT) and

  vision analytics

  for object detection, classification, and OCR
• Proven ability to deploy models on

  Azure and on-prem environments

  with GPU acceleration
• Familiarity with

  predictive modelling techniques

  in manufacturing/engineering contexts
• Excellent communication and stakeholder engagement skills

Desirable Skills (Good to Have)

• Experience with

  digital twin models

  ,

  edge computing

  , or

  smart factory systems

• Knowledge of

  Hugging Face

  ,

  vLLM

  , or other open-source LLM frameworks
• Exposure to

  MLOps tools

  , API development (FastAPI), and automated model retraining
• Awareness of

  data security

  ,

  model interpretability

  , and

  ethical AI practices