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Cloud & AI Platform Engineer
Cloud & AI Platform Engineer
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Cloud & AI Platform Engineer

Faisal AshrafNomani's profile picture
Posted by Faisal AshrafNomani
4 - 10 yrs
Best in industry
Bengaluru (Bangalore), Chennai
Skills
AWS
Windows Azure
Google Cloud Platform (GCP)
Large Language Models (LLM)
AI Agents
Data engineering
DevOps

Job Description:

 

We are seeking a Cloud & AI Platform Engineer to design and operate AI-native infrastructure that supports large-scale machine learning, generative AI, and agentic AI systems. 

This role will focus on building secure, scalable, and automated multi-cloud platforms across AWS, Azure, GCP, and hybrid on-prem environments, enabling teams to deploy LLMs, AI agents, and data-driven applications reliably in production. 

You will work at the intersection of cloud engineering, MLOps, LLMOps, DevOps, and data infrastructure, helping build platforms that support RAG pipelines, vector search, AI model lifecycle management, and AI observability

 

Key Responsibilities

AI & Agentic Infrastructure 

  • Design infrastructure to support agentic AI systems, autonomous agents, and multi-agent workflows. 
  • Build scalable runtime environments for LLM orchestration frameworks. 
  • Enable deployment of AI copilots, assistants, and autonomous decision systems. 

Common frameworks may include: 

  • LangChain 
  • LlamaIndex 
  • AutoGPT 

 

LLMOps & AI Model Lifecycle 

Design and manage LLMOps pipelines for the full lifecycle of large language models: 

  • Model deployment 
  • Prompt management 
  • Versioning 
  • Evaluation and testing 
  • Model monitoring 

Integrate with AI platforms such as: 

  • Azure Machine Learning 
  • Amazon SageMaker 
  • Vertex AI 

 

Retrieval-Augmented Generation (RAG) Infrastructure 

Design and optimize RAG pipelines that integrate enterprise knowledge with LLMs. 

Responsibilities include: 

  • Document ingestion pipelines 
  • Embedding generation workflows 
  • Knowledge indexing 
  • Query orchestration 
  • Retrieval optimization 
  • Support scalable semantic search architectures. 

 

Vector Database & Knowledge Infrastructure 

Deploy and manage vector databases used for AI applications and semantic retrieval. 

Common technologies include: 

  • Pinecone 
  • Weaviate 
  • Milvus 
  • FAISS 

Responsibilities include: 

  • Index optimization 
  • Query latency tuning 
  • Scalable embedding storage 
  • Hybrid search architecture 

 

Multi-Cloud AI Infrastructure 

Design and maintain AI-ready infrastructure across: 

  • Amazon Web Services 
  • Microsoft Azure 
  • Google Cloud Platform 

Key responsibilities include: 

  • GPU infrastructure management 
  • Distributed training environments 
  • Hybrid cloud integrations with on-prem data centers 
  • Infrastructure scaling for AI workloads 

 

Data Platforms & Integration 

  • Support deployment and optimization of data lakes, data warehouses, and streaming platforms. 
  • Work with data engineering teams to ensure secure and scalable data infrastructure. 

 

Cloud Architecture & Infrastructure 

  • Design and implement scalable multi-cloud infrastructure across Azure, AWS, and Google Cloud. 
  • Build hybrid cloud architectures integrating on-premise environments with cloud platforms. 
  • Implement high availability, disaster recovery, and auto-scaling architectures for AI workloads. 

 

DevOps, Platform Engineering & Automation 

Build automated cloud infrastructure using modern DevOps practices. 

Tools may include: 

  • Terraform 
  • Docker 
  • Kubernetes 
  • GitHub Actions 

Responsibilities include: 

  • Infrastructure as Code (IaC) 
  • Automated deployments 
  • CI/CD pipelines for AI models and services 
  • Platform reliability and scalability 

 

AI Observability & Monitoring 

Implement observability frameworks to monitor AI systems in production. 

This includes: 

  • Model performance monitoring 
  • Prompt evaluation 
  • Hallucination detection 
  • Latency and throughput analysis 
  • Cost monitoring for LLM usage 

Tools may include: 

  • Arize AI 
  • WhyLabs 
  • Weights & Biases 

 

Security, Governance & Responsible AI 

Ensure AI systems follow strong governance and security practices. 

Responsibilities include: 

  • Data privacy and compliance 
  • Model governance frameworks 
  • Secure model deployment 
  • Monitoring model bias and drift 
  • AI risk management 

Support enterprise frameworks for Responsible AI and AI compliance. 

 

Data & Security 

  • Experience with data lake architectures, distributed storage, and ETL pipelines 
  • Knowledge of data security, encryption, IAM, and compliance frameworks 
  • Familiarity with AI governance and responsible AI practices 

 

 

Required Skills 

Cloud & Infrastructure 

  • Strong experience in Azure (must have), AWS or GCP 
  • Hybrid and multi-cloud architecture 
  • GPU infrastructure management 

DevOps & Automation 

  • Kubernetes 
  • Docker 
  • Terraform 
  • CI/CD pipelines 

AI / ML Platforms 

  • MLOps pipelines 
  • Model deployment 
  • Model monitoring 

AI Application Infrastructure 

  • Vector databases 
  • RAG pipelines 
  • LLM orchestration frameworks 

Programming 

Experience in one or more languages: 

  • Python 
  • Go 
  • Java 
  • TypeScript 

 

 

 

Preferred Qualifications 

  • Experience building AI copilots or autonomous agents 
  • Knowledge of distributed model training - Knowledge of GPU infrastructure and distributed training 
  • Familiarity with AI evaluation frameworks - Familiarity with model monitoring, drift detection, and AI observability 
  • Experience building enterprise AI platforms 

 

Education & Experience 

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 
  • 4–8+ years experience in cloud infrastructure, DevOps, or platform engineering 
  • Experience working in data-driven or AI-focused environments 

 

 

What Success Looks Like 

  • Reliable ML model deployment pipelines - Reliable infrastructure for LLMs and AI agents, Scalable RAG knowledge platforms 
  • Efficient multi-cloud infrastructure management - Fast deployment cycles for AI products 
  • Secure and scalable AI-ready cloud platforms 
  • Strong automation and governance across cloud and AI systems 


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Shubham Vishwakarma

Full Stack Developer - Averlon
I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About Ampera Technologies

Founded :
2024
Type :
Services
Size :
20-100
Stage :
Profitable

About

At Ampera Technologies, we empower businesses with cutting-edge data analytics, quality assurance, and data engineering solutions

Read more

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