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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