Cloud & AI Platform Engineer

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