AI Operations and RAG Architect

Job Summary

We are seeking an experienced AI Operations & RAG Architect to lead the design, deployment, and optimization of enterprise-grade Artificial Intelligence solutions powered by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures. The ideal candidate will have expertise in AI infrastructure, vector databases, MLOps, cloud platforms, and scalable AI application development.

The AI Operations & RAG Architect will be responsible for building intelligent AI systems that leverage proprietary knowledge sources, ensuring high performance, security, scalability, and reliability across enterprise environments.

Job Description

As an AI Operations & RAG Architect, you will architect and implement advanced AI solutions that combine LLMs, vector search technologies, and enterprise data platforms. You will collaborate with AI Engineers, Data Scientists, Software Developers, Security Teams, and Business Stakeholders to deliver production-ready AI applications and establish best practices for AI operations.

You will play a key role in designing Retrieval-Augmented Generation frameworks, managing AI infrastructure, optimizing model performance, and ensuring responsible AI governance across the organization.

Key Responsibilities

RAG Architecture & Development

• Design and implement enterprise Retrieval-Augmented Generation (RAG) systems.
• Build intelligent search and knowledge retrieval solutions using vector databases.
• Develop document ingestion, indexing, chunking, and embedding pipelines.
• Optimize retrieval accuracy, relevance, and response quality.
• Design scalable AI architectures supporting multiple LLM providers.

AI Operations & Infrastructure

• Manage AI application deployment and production environments.
• Monitor AI model performance, latency, uptime, and reliability.
• Establish AI observability, logging, and monitoring frameworks.
• Implement MLOps and LLMOps best practices.
• Automate AI workflows and deployment pipelines.

Large Language Models (LLMs)

• Integrate commercial and open-source LLMs into enterprise applications.
• Fine-tune and evaluate foundation models for business use cases.
• Implement prompt engineering and prompt management strategies.
• Optimize token usage, inference performance, and operational costs.
• Evaluate emerging AI models and technologies.

Data & Knowledge Management

• Design data pipelines for AI-powered knowledge systems.
• Manage vector databases and embedding strategies.
• Ensure data quality, governance, and accessibility.
• Implement semantic search and contextual retrieval solutions.
• Support enterprise knowledge management initiatives.

Security & Governance

• Establish AI security and compliance standards.
• Implement access controls and data protection mechanisms.
• Ensure responsible AI usage and governance practices.
• Conduct AI risk assessments and model audits.
• Collaborate with legal and compliance teams on AI regulations.

Cross-Functional Collaboration

• Partner with business leaders to identify AI opportunities.
• Provide technical leadership and architectural guidance.
• Mentor AI Engineers and Data Scientists.
• Create technical documentation and architecture diagrams.
• Present AI strategies and recommendations to stakeholders.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
• 5+ years of experience in AI, Machine Learning, Data Engineering, Cloud Architecture, or related disciplines.
• Hands-on experience designing and deploying RAG-based applications.
• Strong understanding of LLM architectures and Generative AI technologies.
• Experience with AI production systems and enterprise deployments.
• Excellent analytical, problem-solving, and communication skills.

Technical Skills

Programming Languages

• Python
• SQL
• JavaScript (Preferred)

AI & Machine Learning

• Large Language Models (LLMs)
• Generative AI
• Prompt Engineering
• Fine-Tuning
• Embeddings
• RAG Architecture

AI Frameworks

• LangChain
• LangGraph
• LlamaIndex
• CrewAI
• AutoGen

Vector Databases

• Pinecone
• Weaviate
• Chroma
• Milvus
• FAISS

Cloud Platforms

• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)

MLOps / LLMOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD Pipelines

Monitoring & Observability

• LangSmith
• OpenTelemetry
• Prometheus
• Grafana

Preferred Qualifications

• Experience with enterprise AI platforms and AI governance frameworks.
• Knowledge of cybersecurity and secure AI development practices.
• Experience working with multi-agent AI systems.
• Familiarity with data privacy regulations and compliance requirements.
• AI-related certifications or cloud certifications are a plus.

Benefits

• Competitive Salary
• Performance-Based Bonus
• Health, Dental, and Vision Insurance
• 401(k) Retirement Plan
• Paid Time Off
• Flexible Work Environment
• Professional Development Programs
• Training & Certification Reimbursement