Up Ai ml architect Jobs – 143 Open Jobs Today

AI Solution Architect II
HCLTech —Noida, Uttar Pradesh
- Azure
- AWS
- APIs
9d
Senior Analyst - Customer Journey Mapping
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- Microsoft Powerpoint
- Salesforce
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WebSenor —Noida, Uttar Pradesh
- Full-time
- Hybrid work
- Azure
- Oracle
- Computer Science
Quick apply
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WebSenor InfoTech —Noida, Uttar Pradesh
- Full-time
- Remote
- Azure
- Oracle
- Computer Science
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8d
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NTT Ltd —Noida, Uttar Pradesh
- Full-time
- Azure
- Master's degree
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- Sales
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WebSenor InfoTech —Noida, Uttar Pradesh
- Full-time
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WebSenor —Noida, Uttar Pradesh
- Full-time
- Hybrid work
- Azure
- SQL
- Machine learning
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WebSenor —Noida, Uttar Pradesh
- Full-time
- Hybrid work
- Azure
- Oracle
- Computer Science
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4d
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Vinove —Noida, Uttar Pradesh
- Azure
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- System design
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Consultant | APS - BY | Noida | Supply Chain & Network Operations
Deloitte —Noida, Uttar Pradesh
- Azure
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- DevOps
Senior Data Engineer – GenAI & Unstructured Data Pipelines
WebSenor —Noida, Uttar Pradesh
- Full-time
- Hybrid work
- Azure
- Master's degree
- Databases
Quick apply
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NTT Ltd —Noida, Uttar Pradesh
- Full-time
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- Doctoral degree
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EXL Service —Noida, Uttar Pradesh
- Full-time
- Azure
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- Windows
7d
Manager | APS - BY | Noida | Supply Chain & Network Operations
Deloitte —Noida, Uttar Pradesh
- Azure
- Oracle
- DevOps
Data Engineer
WebSenor InfoTech —Noida, Uttar Pradesh
- Full-time
- Azure
- Computer Science
- DevOps
Quick apply
2d

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AI Solution Architect II

HCLTech -
Noida, Uttar Pradesh

Apply Now

Job details

9 days ago

Qualifications

TensorFlow
CI/CD
Image processing
Azure
Cloud architecture
Kubernetes
Enterprise Software
OS Kernels
AWS
Docker
Continuous integration
Deep learning
Natural language processing
APIs
Scalability
Manufacturing
AI
Communication skills
Graph databases

Full job description

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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ai ml architect jobs in up

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