Tcs Hiring For AI & Computer Vision Architect

Tata Consultancy Services; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Tata Consultancy Services in Bengaluru seeks an experienced AI & Computer Vision Architect to design scalable vision systems and to integrate AI into platforms and data pipelines. You will lead end-to-end CV projects across hardware and software, enabling robust production deployments.

The role emphasizes Python, PyTorch, training and inference workflows, and the use of synthetic data and sim-to-real validation to improve model robustness in industrial settings.

Qualifications

• Strong Python programming and ability to design large AI/vision codebases.
• End-to-end computer vision system architecture (training validation deployment).
• Experience with vision-based quality inspection in manufacturing / production lines.
• Solid understanding of model training, evaluation, optimization, and inference.
• Knowledge of synthetic data usage for improving AI model robustness.
• Understanding of simulation-based validation and sim-to-real (Sim2Real) concepts.
• Ability to integrate AI systems with platform, backend, and data pipelines.

Skills

Python programming

Computer Vision

PyTorch

Vision pipelines

Model training & inference

NVIDIA AI Enterprise

TAO Toolkit

NVIDIA Metropolis

Omniverse Replicator

Edge AI

Sim2Real

Industrial vision systems

NIM

Tools

NIM

Job description

Role AI & Computer Vision Architect

Total Experience : 12+ year

Required Technical Skill Set

Computer Vision architecture, AI system design, Python, PyTorch, vision pipelines, model training & inference workflows, NVIDIA AI Enterprise-Metropolis, TAO, NIM

Desired Competencies (Technical/Behavioral Competency)

Must-Have

• Strong Python programming and ability to design large AI/vision codebases
• End-to-end computer vision system architecture (training validation deployment)
• Experience with vision-based quality inspection in manufacturing / production lines
• Solid understanding of model training, evaluation, optimization, and inference
• Knowledge of synthetic data usage for improving AI model robustness
• Understanding of simulation-based validation and sim-to-real (Sim2Real) concepts
• Ability to integrate AI systems with platform, backend, and data pipelines.

Good-to-Have

• Exposure to NVIDIA AI Enterprise
• Exposure to NVIDIA Metropolis (or equivalent vision AI deployment frameworks)
• Exposure to NVIDIA TAO Toolkit (or similar transfer-learning / fine-tuning frameworks)
• Experience with synthetic data generation tools (Omniverse Replicator or equivalent)
• Familiarity with model serving / inference services (NVIDIA NIM or equivalent)
• Experience with edge AI, camera hardware, or industrial vision systems
• Prior exposure to robotics perception or autonomous system