Gen ai Architect

Senior Generative AI Engineer

Employment Type: Permanent with VDart Digital

Work Location: Marathalli, Bengaluru

Job Description

We are seeking a highly skilled Senior Generative AI Engineer with strong expertise in designing, developing, and deploying enterprise-scale AI solutions using Large Language Models (LLMs) and modern Generative AI frameworks. The ideal candidate should have hands-on production experience building scalable GenAI applications, AI agents, autonomous workflows, and Retrieval-Augmented Generation (RAG) systems in cloud-native environments.

This role requires deep technical expertise in LLM orchestration, AI application architecture, prompt engineering, vector databases, MLOps, and production deployment of AI systems. Candidates should have proven experience delivering real-world AI solutions in enterprise environments with strong exposure to cloud platforms and DevOps practices.

Key Responsibilities

• Design, build, and deploy enterprise-grade Generative AI applications using Large Language Models (LLMs).
• Develop intelligent AI agents and autonomous workflows using frameworks such as LangChain, CrewAI, LangGraph, AutoGen, or similar agentic AI frameworks.
• Implement and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and semantic search technologies.
• Work extensively on prompt engineering, tool calling, memory management, agent orchestration, and multi-agent systems.
• Integrate and manage LLMs such as OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar foundation models.
• Develop scalable AI services and APIs using Python and FastAPI.
• Build production-ready AI solutions with high availability, scalability, monitoring, and observability.
• Deploy and manage AI applications in cloud-native environments using Docker and Kubernetes.
• Collaborate with Data Science, ML Engineering, and DevOps teams to operationalize AI solutions.
• Implement CI/CD pipelines and automated deployment processes for AI workloads.
• Monitor model performance, latency, reliability, and operational efficiency in production environments.
• Ensure AI solutions follow enterprise security, governance, and responsible AI standards.
• Evaluate and adopt emerging Generative AI tools, frameworks, and models.

Required Skills

Generative AI & LLM Expertise

• Strong hands-on experience with Generative AI and Large Language Models (LLMs).
• Production-level experience building and deploying GenAI applications.
• Expertise in LangChain, CrewAI, LangGraph, AutoGen, or similar frameworks.
• Experience with AI agents, autonomous workflows, and multi-agent architectures.
• Strong understanding of prompt engineering, embeddings, model evaluation, and LLM orchestration.
• Experience integrating OpenAI, Azure OpenAI, Claude, Gemini, Llama, Mistral, or similar models.

RAG & Vector Databases

• Strong experience implementing RAG pipelines and semantic retrieval systems.
• Experience with vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Milvus.
• Understanding of chunking strategies, embeddings, indexing, reranking, and retrieval optimization.

Python & AI Development

• Strong proficiency in Python.
• Experience with FastAPI for AI service and API development.
• Experience with AI/ML libraries and data processing tools such as Pandas and NumPy.

Cloud & Production Deployment

• Mandatory production experience on at least one cloud platform:
• Microsoft Azure
• Experience deploying scalable AI applications in enterprise production environments.
• Hands-on experience with Docker, Kubernetes, Jenkins, Terraform, and CI/CD pipelines.
• Strong understanding of MLOps, AI deployment lifecycle, monitoring, and observability.

Engineering & Operational Excellence

• Strong understanding of software engineering best practices.
• Experience with Git, version control, automated testing, and release management.
• Experience building secure, scalable, and high-performance AI solutions.
• Ability to troubleshoot production AI systems and optimize performance.

Preferred Skills

• Experience with AI observability and evaluation frameworks.
• Exposure to fine-tuning, PEFT, LoRA, or model optimization techniques.
• Experience with enterprise AI governance and responsible AI practices.
• Knowledge of distributed AI systems and scalable inference architectures.
• Familiarity with AI security and compliance standards.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• 3–8 years of overall software engineering experience.
• Minimum 3+ years of hands-on experience in Generative AI and LLM-based application development,
• Proven track record of delivering enterprise-scale AI solutions in production environments.
• Strong communication and stakeholder management skills.

We’re on hunt for AI Architect

Responsibilities:

• 10–15+ years overall experience, with recent hands-on AI/GenAI architecture ownership.
• Must have architected enterprise AI platforms/solutions end-to-end, not just individual ML models or PoCs.
• Strong GenAI/LLM production experience: RAG, embeddings, vector DBs, hybrid search, reranking, evaluation, guardrails.
• Strong Agentic AI understanding: agents, tool calling, workflows, orchestration, human-in-the-loop.
• Experience taking AI solutions from architecture → production → scale, ideally across multiple business teams/use cases.
• Strong cloud architecture — Azure/AWS preferred; hybrid/on-prem experience is a plus.
• Must understand enterprise security, governance, Responsible AI, observability and LLMOps/MLOps.
• Should be able to articulate build-vs-buy, MVP-vs-target architecture, cost/performance/security tradeoffs.
• Strong stakeholder-facing / consulting ability — can work with business leaders, engineering, security and data teams and influence without authority.

There is scope to move to the US for this role if you are aligned for the same, else this will be a WFO role from Hyderabad location

POSITION OVERVIEW

We are seeking an experienced Senior Data Scientist & Generative AI Specialist on a contractual basis to support a premier Germany-based chemical manufacturing enterprise. In this role, you will lead the end-to-end design, development, and deployment of production-grade GenAI applications, multi-modal LLM workflows, and advanced retrieval platforms tailored to complex industrial and enterprise data ecosystems.

Working closely with cross-functional global teams, you will build robust backend microservices, implement state- of-the-art RAG/GraphRAG architectures, and leverage cloud-native AI infrastructure (Azure, Vector DBs, Knowledge Graphs) to drive operational efficiency and data-driven innovation.

KEY RESPONSIBILITIES

• GenAI & LLM System Engineering: Design, build, and deploy production-grade multi-modal GenAI applications processing text, structured technical documentation, images, and telemetry data.
• Advanced RAG & Graph Architecture: Implement cutting-edge Retrieval-Augmented Generation (RAG) and GraphRAG pipelines using document parsing frameworks, custom embeddings, vector databases, and knowledge graphs to capture complex domain relationships.
• Scalable Backend Development: Architect high-throughput, low-latency microservice APIs using Python, FastAPI, and Flask, leveraging asynchronous programming (asyncio) and strict type validation (Pydantic) for long-running LLM processes.
• Agentic Systems & Azure Ecosystem: Build autonomous agent systems using modern frameworks (MCP, A2A) and orchestrate enterprise workflows across the Microsoft Azure AI ecosystem (Azure AI Foundry, AI Search, Document Intelligence, Databricks).
• Model Optimization & Evaluation: Execute systematic LLM fine-tuning, prompt optimization, and rigorous evaluation frameworks to assess AI output accuracy, reliability, and business impact against industrial requirements.
• Data Layer Management: Architect and maintain enterprise database layers combining SQL (PostgreSQL) for structured transactional data with specialized vector search engines and graph stores.
• Rapid Prototyping: Utilize AI-assisted development tools (Copilot, Claude Code) to accelerate delivery timelines and rapidly build functional UI prototypes for client feedback.

TECHNICAL QUALIFICATIONS

Core Development & Backend:

• Python Mastery: Deep expertise in writing clean, production-ready Python using asynchronous programming (asyncio), strict type-hinting (Pydantic), and automated testing patterns.

• Backend Microservices: Hands-on experience building microservices with FastAPI and Flask structured to handle asynchronous, long-running AI background tasks.

• Database Engineering: Strong command of PostgreSQL, relational schema design, vector indexing, and knowledge graph paradigms.

Machine Learning & AI Infrastructure:

• Model Expertise: Hands-on experience with leading multi-modal LLM architectures (OpenAI, Anthropic, Google) and domain-specific AI workflows.

• Retrieval & Parsing: Proven track record with document extraction frameworks, embedding models, vector search engines, and GraphRAG architectures.

• Cloud Infrastructure: Strong proficiency with Azure AI infrastructure (Foundry, Databricks, AI Search, Document Intelligence).

• Agentic Frameworks: Practical experience with open-source agent protocols (MCP, A2A), parameter-efficient fine-tuning (PEFT/LoRA), and model evaluation methodology.

CONTRACT & REMOTE REQUIREMENTS

• Contract Engagement: Contractual structure tailored to project milestones and deliverables.

• 100% Remote Setup: Fully equipped home office with high-speed, secure internet infrastructure.

• Timezone Overlap: Guaranteed 4-hour daily overlap with Central European Time (CET/CEST - Germany) to ensure smooth collaboration with enterprise stakeholders.

• Communication: Fluent professional English communication skills (written and spoken) for asynchronous and real-time technical coordination. 

Job Summary:

Wissen Technology is hiring an AI Implementation Engineer to build, deploy, and scale enterprise-grade Generative AI solutions across business-critical applications. The role involves developing production-ready AI systems using Azure AI services, Large Language Models (LLMs), RAG architectures, and agent-based frameworks while collaborating closely with engineering teams to drive AI adoption and innovation.

Experience

6-12 years

Location

Mumbai / Bangalore

Mode of Work

Hybrid

Mandatory Skills (Must Have)

• Python programming (6+ years) including asynchronous programming and backend application development

• Java and Spring Framework (3+ years) for enterprise-scale application integration

• Azure OpenAI Service, Azure AI Foundry, and Azure AI Search for production GenAI applications

• Retrieval Augmented Generation (RAG) architecture including embeddings, chunking, vector databases, reranking, and grounding techniques

• Agent Frameworks such as Microsoft Agent Framework, Semantic Kernel, AutoGen, LangChain, or LangGraph

• Snowflake and Cortex AI (Cortex Search, LLM Functions) with strong SQL expertise

• Prompt Engineering, LLM evaluation frameworks, testing, and model performance optimization

• DevOps and Cloud Deployment using Azure DevOps, GitHub Actions, Docker, AKS, Azure Functions, and observability tools

Optional Skills (Good to Have)

• React.js for AI-powered user interfaces and conversational applications

• Azure AI Content Safety and Responsible AI implementation experience

• Financial Services, Banking, or other regulated industry domain experience

• Real-time streaming applications and token-level LLM operations

• Performance optimization, caching strategies, and cost optimization for AI workloads

• Microsoft Azure AI Engineer Associate Certification

Location: Pune / Gurgaon

Position: AI Engineer

work mode: WFO

  Job Description.

​

 Job responsibilities:

• Responsibility for design, implementation and deployment of Generative AI, Agentic frameworks at scale
• Strong in programming - Python a
• Previous experience of working on Computer Vision projects and VLM /VLAM models.
• In depth awareness of Transformer architectures and End to End Deep neural networks
• Full stack AI / ML development experience
• Design, build & maintain efficient and reliable Agentic / Generative AI code leveraging pipelines
• Hosting and deployment knowledge in GCP or AWS or Azure along with advanced engineering concepts to build user friendly UI interface for easy adoption.

    Requirements:

 ·      4 to 8 years overall years of experience (Agentic AI, Generative AI, VLM, VLAM and LLM) with significant exposure in Development, Architecture design, scaling and hosting in cloud.

    Must Have –

 ·      Architecting and solutioning experience with Python and FAST API, Agentic Ai frameworks, VLMs, VLAMs, Open source LLM’s and Code based LLM models at scale with - Langchain /      Ollama, embeddings, Memory      Management etc.,

·      Practical experience in implementing Explainable and ethical AI models  Practical experience in implementing frameworks like RAG/ CAG/ Self-reflective RAG etc.,

·      Experience in cloud hosting either AWS or Azure or GCP.

·      Experience in ML-OPS - Implement a feedback mechanism to continually improve the model over time through feedback loop and monitoring KPI’s in production.

·      Experience with Quantization and Kubernetes or docker

    Good to have

·      gRPC implementation to expose the API’s on a server for easy usage and good user interface

·      Streamlit front end creation

·      Experience with SAFe framework deliveries.

What We Are Looking For

CLOUDSUFI is seeking a senior, hands-on AI Platform Architect to design and build production-grade platforms for generative AI, agentic systems, data-intensive applications, and analytical workflows. This is a builder-architect role. The successful candidate will define architecture, make technology decisions, develop reference implementations, review critical code and designs, and guide engineering teams from prototypes to secure, scalable production systems. We are looking for a builder-architect with strong engineering judgement and practical delivery experience. The right candidate can define platform direction, evaluate trade-offs, validate ideas through implementation, and guide systems into production. They should be equally comfortable discussing distributed architecture, reviewing code, diagnosing workflow failures, designing evaluation systems, and mentoring engineering teams.

Key Responsibilities-

AI and Agentic Platform Architecture

• Design platforms for single-agent and multi-agent systems supporting planning, reasoning, tool use, memory, delegation, validation, and human approval.

• Define orchestration patterns for deterministic, dynamic, event-driven, and long-running AI workflows.

• Establish clear boundaries between LLM reasoning, application logic, quantitative computation, rules, and human decision-making.

• Evaluate and adopt agent frameworks, model providers, tools, and orchestration technologies based on reliability, flexibility, performance, and cost. Knowledge and Data Systems

• Architect RAG pipelines, document-processing systems, vector search, hybrid retrieval, knowledge graphs, and semantic data layers.

• Integrate structured and unstructured enterprise data from APIs, databases, files, streams, and external platforms.

• Design reusable workflows for research, data collection, transformation, analysis, modelling, validation, and reporting.

• Establish data lineage, provenance, metadata, access controls, freshness, and quality standards. Evaluation, Observability and Governance

• Build evaluation frameworks for accuracy, relevance, groundedness, task completion, tool use, safety, latency, and cost.

• Enable systematic experimentation across models, prompts, agents, tools, retrieval strategies, and orchestration patterns.

• Implement versioning and lifecycle management for prompts, agents, workflows, datasets, knowledge bases, evaluations, and model configurations.

• Establish tracing, monitoring, auditability, guardrails, approval workflows, and production quality diagnostics.

Cloud and Platform Engineering

• Define cloud-native architectures using microservices, APIs, event-driven systems, queues, schedulers, and distributed processing.

• Lead Kubernetes-based deployment, containerisation, CI/CD, Infrastructure as Code, environment management, and release automation.

• Design for horizontal scalability, fault tolerance, resilience, security, data privacy, and high availability.

• Optimise model usage, infrastructure, storage, retrieval, and compute for performance, latency, and cost.

Technical Leadership

• Translate product and business requirements into clear technical designs and implementation plans.

• Build prototypes and reference implementations for high-risk or foundational platform capabilities.

• Review architecture, code, interfaces, data models, infrastructure, and operational readiness.

• Define engineering standards and reusable patterns across AI, backend, data, and platform teams.

• Mentor senior engineers and support teams in resolving complex technical and production issues.

Required Skills and Experience

• 10+ years of experience in software architecture, platform engineering, distributed systems, data platforms, or AI systems.

• Strong hands-on experience designing and building production-grade AI or data-intensive platforms.

• Deep understanding of LLM applications, tool calling, structured outputs, RAG, embeddings, memory, and agent orchestration.

• Strong experience with cloud platforms, Kubernetes, containers, microservices, APIs, event driven architecture, CI/CD, and Infrastructure as Code.

• Experience with relational, document, graph, vector, and distributed data systems.

• Practical experience implementing AI evaluation, experimentation, tracing, monitoring, guardrails, and lifecycle management.

• Strong understanding of security, identity, access control, secrets management, data protection, and production reliability.

• Ability to move effectively between architecture, code, infrastructure, debugging, and technical delivery.

Good to Have

• Experience building enterprise AI copilots, autonomous workflows, research platforms, or analytical systems.

• Experience with knowledge graphs, hybrid search, model gateways, tool gateways, or agent marketplaces.

• Familiarity with LLMOps, MLOps, model serving, feature stores, model registries, and distributed compute.

• Experience supporting real-time and batch data processing at scale.

• Experience comparing and operating multiple commercial and open-source models.

• Prior experience in consulting, client-facing architecture, or complex enterprise platform delivery. 

About the Role:

We are looking for an ideal candidate with 5+ years of experience in Data Science / Machine Learning, with strong hands-on experience in Generative AI, Large Language Models (LLMs), NLP, and AI-powered applications. The candidate should be comfortable working across the complete AI lifecycle—from understanding business requirements and experimenting with models to building, evaluating, deploying, and monitoring production-grade GenAI solutions.

The role requires a combination of strong technical expertise, business understanding, problem-solving ability, and stakeholder management skills.

Key Responsibilities:

Generative AI & LLM

·      Design, develop, and deploy Generative AI and LLM-based solutions for enterprise use cases.

·      Work with models such as OpenAI, Azure OpenAI, Llama, Mistral, Gemini, or equivalent LLM platforms.

·      Develop applications using prompt engineering, structured outputs, function/tool calling, and LLM orchestration.

·      Design and implement Retrieval-Augmented Generation (RAG) solutions.

·      Work with vector databases and semantic search for enterprise knowledge retrieval.

·      Develop and evaluate AI agents and multi-step AI workflows.

·      Implement techniques such as prompt optimization, context management, grounding, and hallucination reduction.

·      Develop AI solutions for text classification, summarization, information extraction, question answering, document intelligence, and other enterprise use cases.

Machine Learning & Data Science

·      Develop and optimize traditional Machine Learning and statistical models where appropriate.

·      Perform data exploration, feature engineering, model selection, training, validation, and evaluation.

·      Apply appropriate ML and statistical techniques to solve business problems.

·      Work with structured, unstructured, and semi-structured data.

·      Develop scalable data pipelines to support AI/ML solutions.

·      Collaborate with Data Engineers to prepare and manage data for AI applications.

AI Evaluation & Productionization

·      Design evaluation frameworks to measure LLM accuracy, relevance, groundedness, toxicity, latency, and cost.

·      Implement guardrails and responsible AI practices.

·      Monitor model and application performance in production.

·      Identify model/data drift and implement appropriate improvement strategies.

·      Optimize AI solutions for performance, scalability, reliability, and cost.

·      Support deployment and productionization of AI/ML solutions.

·      Client & Delivery Responsibilities

·      Work closely with the CEO, Delivery team, Solution Architects, Engineering teams, and clients to understand business problems and identify AI opportunities.

·      Translate business requirements into practical AI/ML solutions.

·      Participate in client discussions, solution presentations, technical workshops, and POCs.

·      Develop rapid prototypes and demonstrate the feasibility of GenAI solutions.

·      Convert successful POCs into scalable, production-ready applications.

·      Provide technical guidance and contribute to AI solution architecture.

·      Prepare technical documentation, solution approaches, and project estimates where required.

·      Stay current with developments in Generative AI, LLMs, Agentic AI, and AI engineering.

Required Skills:

·       5+ years of hands-on experience in Data Science, Machine Learning, AI, or a related field.

·      Strong practical experience in Generative AI and LLM-based applications.

·      Strong proficiency in Python.

·      Strong understanding of Machine Learning and statistical concepts.

·      Hands-on experience with:

o       LLMs

o       Prompt Engineering

o       RAG

o       Vector Databases

o       Embeddings

o       Semantic Search

o       LLM Evaluation

o       AI Guardrails

·      Experience with frameworks/tools such as LangChain, LangGraph, LlamaIndex, or equivalent.

·      Experience with APIs and integrating LLMs into enterprise applications.

·      Strong SQL and data handling skills.

·      Experience working with large and complex datasets.

·      Strong understanding of NLP concepts.XX

Technical Skills:

·      Experience with OpenAI / Azure OpenAI / AWS Bedrock / Google Vertex AI.

·      Experience with vector databases such as Pinecone, Weaviate, Milvus, FAISS, or equivalent.

·      Experience with Databricks, Snowflake, or cloud data platforms.

·      Experience with Docker and CI/CD.

·      Exposure to AWS, Azure, or GCP.

·      Experience with ML/AI deployment and MLOps.

·       Knowledge of AI security, data privacy, governance, and responsible AI.

·      Experience building AI Agents / Agentic AI workflows.

·      Experience with multimodal AI is an added advantage

Key Competencies

·      Strong analytical and problem-solving ability.

·      Ability to translate business problems into practical AI solutions.

·      Strong communication and presentation skills.

·      Ability to interact confidently with senior stakeholders and clients.

·      Strong ownership and delivery mindset.

·      Ability to work independently in a fast-paced environment.

• Strong experimentation and innovation mindset.
• Ability to balance technical feasibility, business value, scalability, and cost.

Required Education & Experience:

·      Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline

Key Responsibilities:

·      Architectural Leadership: Design and lead the development of robust, scalable AI architectures, ensuring high performance, reliability, and security.

·      Applied Mathematics & Statistics: Apply statistical analysis, numerical computation, and mathematical modeling to derive insights from large-scale data and optimize model performance.

·      Deep Learning Development: Design, train, and deploy advanced Deep Learning (DL) models.

·      Technical Mentorship: Mentor engineering teams on best practices for AI/ML, coding standards, and architectural design.

·      Model Optimization: Optimize models for speed, efficiency, and accuracy using techniques like pruning, quantization, or GPU acceleration.

·      Strategy & Innovation: Evaluate and select appropriate AI frameworks, tools, and platforms, staying abreast of cutting-edge research and industry trends.

Qualifications:

Required:

·      Education: Master's or PhD in Computer Science, Applied Mathematics, Statistics, Physics, or a related quantitative field.

·      Experience: 10+ years of experience in software development, with at least 3-5 years in a Applied Mathematics and Deep learning.

·      AI/ML Expertise: Proven experience designing and deploying deep learning models in production using frameworks.

·      Mathematics/Statistics: Strong proficiency in linear algebra, calculus, probability, and statistical methods.

·      Programming Skills: Expert-level coding skills in Python (NumPy, Pandas, Scikit-learn) and experience with languages like Java or C++.

Key Competencies:

• Strategic mindset with deep operational awareness.
• Excellent communication and stakeholder management skills.
• Ability to simplify complex technical concepts for executive reporting.
• Strong leadership, people development, and cross-functional influencing skills.

Bias for action and a relentless focus on continuous improvement.

Location: Jaipur (Work From Office)

Employment Type: Full-Time

We're looking for a GenAI Engineer (LLM Engineer) to build scalable AI-powered SaaS applications using Large Language Models (LLMs). You'll develop intelligent AI workflows, integrate LLMs into production systems, and build secure, high-performance AI solutions.

Key Responsibilities

• Integrate LLM APIs (OpenAI, Claude, Hugging Face) into production applications.
• Design and optimize RAG pipelines and prompt engineering workflows.
• Build and manage Vector Databases (Pinecone, Weaviate, pgvector).
• Optimize AI performance, latency, and operational cost.
• Ensure secure, scalable AI architecture.
• Collaborate with Product and Engineering teams to deliver AI-powered features.

Requirements

• 3+ years of backend development using Python, Go, or Node.js.
• Hands-on experience with LLMs, LangChain or LlamaIndex.
• Strong understanding of RAG, Prompt Engineering, and Vector Databases.
• Experience with AWS, GCP, or Azure.
• Knowledge of APIs, Microservices, and AI application development.

Preferred: Experience in SaaS/FinTech, LLMOps, or Model Fine-tuning.

Education: B.Tech, BCA, or equivalent technical qualification.

Apply Now

Application Form: https://zfrmz.com/pAKb2ynfomIsuNwRfRbV?utm_source=cutshort

Job Description – AI Engineer (End-to-End Development & Deployment)

Role Summary

We are looking for an AI Engineer with hands-on experience in designing, developing, deploying, and maintaining Generative/Agentic AI solutions in production. The ideal candidate should have end-to-end ownership of AI applications, from development to deployment, monitoring, and optimization.

Key Responsibilities

●        Design, build, and deploy Generative/Agentic AI solutions.

●        Develop applications using LLMs, RAG, AI agents, and vector databases.

●        Build scalable APIs and integrate AI solutions with enterprise applications.

●        Implement CI/CD pipelines, containerization, and MLOps best practices.

●        Monitor, optimize, and maintain production AI systems.

●        Collaborate with cross-functional teams to deliver business-driven AI solutions.

Required Skills

●       Strong programming skills in Python.

●       Experience with vector databases (e.g., Pinecone, FAISS, ChromaDB) and graph memory systems

●       Knowledge of atleast one agent development framework: Google ADK (preferred), LangChain/LangGraph/LlamaIndex, CrewAI

●       Experience with LLMs, RAG, GenAI, AgenticAI Agents

●       Hands-on experience with FastAPI, and REST APIs.

●       Knowledge of Docker, Kubernetes, Git, CI/CD.

●       Experience with AWS, Azure, or GCP. 

●       Experience with security compliance, monitoring and observability tools such as AWS CloudWatch, Azure Monitor, Google Cloud Monitoring.