Noida, Uttar Pradesh
Job Summary
We are looking for an AI Enterprise Architect to lead the architecture, design, and standardization of our Physical AI platform (e.g., VisionX) and its deployment across enterprise environments. This role will define the end-to-end architecture for AI systems that operate in the physical world—combining computer vision, edge AI, IoT/OT integration, cloud services, digital twin, and agentic AI .
You will work across product engineering, platform teams, and vertical account teams to ensure our solutions are scalable, reusable, secure, and production-ready . You’ll be expected to balance innovation with operational rigor , and align architecture decisions with business outcomes and delivery realities.
Key Responsibilities
Key Responsibilities 1. Enterprise AI & Physical AI Architecture Define end-to-end architecture across: Edge AI (camera-based systems, IoT, GPU devices) Cloud AI platforms Enterprise systems integration Design architectures for: VisionX-like platforms (video analytics, safety AI, inspection) Digital twin / simulation systems (Omniverse, etc.) Establish architecture principles: modularity, reusability, scalability platform-agnostic design (avoid vendor lock-in) edge-cloud hybrid deployment 2. Generative AI & Multimodal Systems Architect solutions using: LLMs (OpenAI, Claude, Gemini, open-source) Multimodal models (text + image + video) Design: Retrieval-Augmented Generation (RAG) enterprise knowledge systems copilots and conversational interfaces Define: prompt engineering strategies grounding mechanisms evaluation frameworks 3. Agentic AI & Autonomous Systems Design agent-based systems: multi-agent orchestration task planning and execution tool/API integration Build frameworks for: autonomous workflows decision-making systems human-in-the-loop control Evaluate and integrate: LangChain, LangGraph, Semantic Kernel, custom frameworks 4. Computer Vision & Edge AI Systems Architect real-time AI systems for: object detection, tracking, activity recognition multi-camera video analytics Define edge AI strategies: on-device vs centralized inference latency and throughput optimization Design systems using: NVIDIA DeepStream, Triton, TensorRT Jetson / GPU-based edge devices 5. AI Platform, MLOps & DevOps Define platform architecture for: model lifecycle (training deployment monitoring) CI/CD for AI and edge systems Establish: model versioning and registry drift detection and feedback loops deployment automation (OTA updates for edge) 6. Data, Integration & Knowledge Systems Design unified data architecture: structured + unstructured + streamin
Skill Requirements
AI / ML Foundations
Strong expertise in:
ML, deep learning
NLP, computer vision
Hands-on with:
PyTorch / TensorFlow
Generative AI
LLMs, prompt engineering
RAG architectures
embeddings, vector DBs (FAISS, Pinecone)
Agentic AI
multi-agent systems
orchestration frameworks (LangChain, LangGraph, etc.)
tool-calling and workflow automation
Computer Vision & Edge AI
video analytics pipelines
DeepStream, Triton
TensorRT optimization
edge devices (Jetson, GPUs)
Systems & Cloud
microservices architecture
APIs, event-driven systems
Kubernetes, Docker
cloud platforms (Azure/AWS/GCP)
Data & Integration
data engineering concepts
knowledge graphs (preferred)
enterprise system integration
Architecture & Governance
enterprise architecture frameworks
security and compliance
scalability and reliability design
Preferred Skills
Digital twin / simulation (Omniverse)
IoT / OT systems (SCADA, PLCs)
Experience in manufacturing / industrial domains
Experience building enterprise AI platforms
Other Requirements
Leadership & Behavioral Competencies
Strong systems thinking and ability to simplify complexity.
Ability to balance innovation with practical delivery constraints.
Excellent communication skills—can explain architecture to engineers, business stakeholders, and customers.
Strong collaboration and influence across matrix organizations.
Ownership mindset with focus on measurable business outcomes
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