Vision AI Solution Architect

Tata Consultancy Services; Bengaluru; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

Tata Consultancy Services seeks a Vision Solution Architect to lead end-to-end computer vision and multimodal GenAI projects. You will design, implement and deploy architectures across cloud and edge environments, guiding model selection, fine-tuning and productionization for real-world use cases.

The role requires strong Python, PyTorch, and MLOps collaboration, with experience across hyperscalers and edge devices. Bengaluru-based full-time position.

Qualifications

• CLIP and contrastive embedding models; VLMs for captioning, visual QA and grounding.
• Design Vision RAG systems with multimodal embeddings and vector databases.
• Fine-tune/adapt vision/VLMs using LoRA/QLoRA and transfer learning.
• Experience with two or more cloud vision services (AWS, GCP, Azure).
• Edge AI deployment with accelerators and quantization techniques.
• Proficient in Python; strong PyTorch, HF Transformers, and OpenCV skills.
• Familiar with ONNX/TensorRT/OpenVINO/CoreML tooling and formats.
• Knowledge of Docker/Kubernetes for scalable serving and orchestration.

Responsibilities

• Architect end-to-end CV and multimodal GenAI solutions from discovery to deployment.
• Lead design of CLIP-style embeddings, VLMs, and traditional CV models.
• Build Vision RAG pipelines with embedding retrieval and grounding.
• Guide fine-tuning and domain adaptation for accuracy in production.
• Evaluate services across AWS, GCP, Azure; balance managed vs self-hosted.
• Architect edge deployments on Jetson/edge TPU with model compression.
• Collaborate with data science, MLOps and client teams; translate business needs.
• Document architectures, accelerators, and reusable patterns.

Skills

CLIP embedding models

Vision-Language Models

Vision RAG systems

LoRA/QLoRA fine-tuning

Hyperscaler AI/vision services

Edge AI deployment

Python

PyTorch

Hugging Face Transformers

OpenCV

ONNX/TensorRT/OpenVINO/CoreML

Docker

Kubernetes

Vision model serving

Architectural communication

Tools

PyTorch

Hugging Face Transformers

OpenCV

ONNX

TensorRT

OpenVINO

CoreML

Docker

Kubernetes

Job description

Computer Vision, VLMs & Multimodal GenAI Architecture

Experience Level: 8-12 years

Employment Type: Full-Time

About the Role

We are looking for a Vision Solution Architect to design and lead end-to-end computer vision and multimodal AI solutions — spanning classical vision models, CLIP-style embedding models, Vision-Language Models (VLMs), and Vision RAG architectures. The ideal candidate can architect solutions across cloud hyperscalers and edge deployments, guiding fine-tuning, integration, and productionization of vision and multimodal GenAI systems for real-world business use cases.

Key Responsibilities

• Architect end-to-end vision and multimodal GenAI solutions, from use-case discovery and model selection through deployment and monitoring.
• Design solutions leveraging CLIP-style embedding models, VLMs (e.g., LLaVA, Gemini Vision, GPT-4V/Claude Vision, Qwen-VL), and traditional CV models (detection, segmentation, OCR).
• Design and implement Vision RAG pipelines — multimodal embedding generation, vector indexing/retrieval, and grounding of VLM outputs against visual and textual knowledge bases.
• Lead fine-tuning and domain adaptation of vision and vision-language models (LoRA/QLoRA, adapter-based tuning, contrastive fine-tuning) for domain-specific accuracy.
• Architect solutions across hyperscaler vision/AI services — AWS (Rekognition, Bedrock multimodal models, SageMaker), GCP (Vertex AI Vision, Gemini multimodal APIs), and Azure (AI Vision, Azure AI Foundry) — selecting the right managed vs. self-hosted approach.
• Design edge deployment architectures for vision models on constrained devices (NVIDIA Jetson, edge TPUs, mobile/embedded accelerators), including model compression and quantization.
• Select and optimize model formats, runtimes, and accelerators (ONNX, TensorRT, OpenVINO, CoreML) for target hardware and latency/throughput requirements.
• Define evaluation frameworks for vision/multimodal model quality — embedding retrieval accuracy, grounding fidelity, hallucination, and task-specific benchmarks.
• Collaborate with data science, MLOps, and client-facing teams to translate business requirements into scalable, cost-efficient vision architectures.
• Document reference architectures, best practices, and reusable accelerators for vision and multimodal GenAI engagements.

Required Skills & Experience

• Strong hands-on experience with CLIP and other contrastive embedding models, and Vision-Language Models (VLMs) for tasks such as captioning, visual QA, and grounding.
• Practical experience designing Vision RAG systems, including multimodal embeddings, vector databases, and retrieval-augmented generation patterns.
• Experience fine-tuning and adapting vision/VLM models using parameter-efficient techniques (LoRA/QLoRA) and classical CV model training/transfer learning.
• Working knowledge of hyperscaler AI/vision services across at least two of AWS, GCP, and Azure, and their trade-offs for vision workloads.
• Familiarity with edge AI deployment — hardware accelerators (NVIDIA Jetson, Coral/edge TPU), model compression, quantization, and runtime optimization.
• Proficiency in Python and vision/ML frameworks (PyTorch, Hugging Face Transformers, OpenCV) and model format/runtime tooling (ONNX, TensorRT, OpenVINO).
• Understanding of containerization and orchestration (Docker, Kubernetes) for scalable vision model serving.
• Strong architectural and communication skills, with the ability to translate business needs into technical vision/GenAI solution designs.