Site Reliability Engineer II

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At American Express, you’ll be recognized for your contributions, leadership, and impact—every colleague has the opportunity to share in the company’s success. Together, we’ll win as a team, striving to uphold our company values and powerful backing promise to provide the world’s best customer experience every day. And we’ll do it with the utmost integrity, and in an environment where everyone is seen, heard and feels like they belong.

Join Team Amex and let's lead the way together.

How will you make an impact in this role?

We are seeking an experienced Site Reliability Engineer to join our Generative AI infrastructure team. This role focuses on ensuring the reliability, scalability, and performance of our RAG (Retrieval-Augmented Generation) systems and agentic AI architectures. The ideal candidate will have 5+ years of SRE experience with specialized expertise in AI/ML infrastructure, particularly in production deployment and operation of large language models, vector databases, and autonomous agent systems.

Key Responsibilities

AI Infrastructure Management & Reliability

• Design, deploy, and maintain highly available RAG pipelines including vector databases, embedding services, and LLM inference infrastructure
• Ensure reliable operation of agentic AI systems including multi-agent orchestration platforms, tool integration frameworks, and decision-making workflows
• Implement comprehensive monitoring and observability for AI model performance, token usage, latency, and accuracy metrics
• Lead incident response for AI system outages, including model degradation, vector search failures, and agent execution issues

RAG System Operations

• Optimize and maintain vector database infrastructure (Pinecone, Weaviate, Chroma, or similar) for high-performance similarity search at scale
• Manage embedding model deployments and ensure consistent document ingestion pipelines with proper chunking and preprocessing
• Implement retrieval quality monitoring, including relevance scoring and context window optimization
• Design and maintain hybrid search systems combining vector and traditional search methodologies

Agentic Architecture Reliability

• Build and maintain infrastructure for autonomous agent systems including planning, reasoning, and tool execution frameworks
• Implement robust error handling and fallback mechanisms for agent decision chains and multi-step workflows
• Monitor and optimize agent performance metrics including success rates, execution time, and resource utilization
• Ensure secure and reliable integration between agents and external APIs, databases, and services

MLOps & Platform Engineering

• Develop Infrastructure as Code solutions for AI/ML workloads including GPU clusters, model serving infrastructure, and data pipelines
• Build automated deployment pipelines for LLM fine-tuning, RAG system updates, and agent workflow modifications
• Implement A/B testing frameworks for AI system improvements and model version management
• Design capacity planning and auto-scaling solutions for variable AI workloads and inference demands

Required Skills & Experience

Generative AI & ML Infrastructure

• 5+ years of SRE/DevOps experience with 2+ years specifically focused on AI/ML production systems
• Deep hands-on experience with RAG architecture implementation including vector databases, embedding models, and retrieval systems
• Proven experience with agentic AI frameworks (LangChain, LlamaIndex, AutoGPT, CrewAI, or similar) and multi-agent orchestration
• Strong understanding of LLM deployment and optimization including model serving frameworks (vLLM, TensorRT-LLM, Triton) and GPU infrastructure management

Vector & Search Technologies

• Proficiency with vector database technologies (PgVector, Pinecone, Weaviate, Qdrant, Chroma, Milvus) and their operational requirements
• Experience with embedding models (OpenAI, Sentence Transformers, Cohere) and semantic search optimization
• Knowledge of hybrid search implementations combining vector, keyword, and graph-based retrieval methods
• Understanding of chunking strategies, document preprocessing, and knowledge graph integration

AI System Monitoring & Observability

• Experience implementing AI-specific monitoring including model drift detection, hallucination tracking, and response quality metrics
• Proficiency with MLOps tools (MLflow, Weights & Biases, Neptune) and experiment tracking systems
• Knowledge of AI system debugging including prompt tracing, agent execution visualization, and performance bottleneck identification
• Understanding of AI safety monitoring including content filtering, bias detection, and usage pattern analysis

Infrastructure & Cloud Platforms

• Proficiency with cloud AI services (AWS SageMaker, Google Vertex AI, Azure ML) and their operational aspects

• Advanced Kubernetes experience including GPU scheduling, resource quotas, and AI workload optimization
• Experience with container technologies optimized for ML workloads and model serving

Programming & Automation

• Proficient in Python with deep understanding of AI/ML libraries (transformers, langchain, llamaindex, torch, numpy)
• Experience with Infrastructure as Code tools (Terraform, Helm) specifically for AI infrastructure provisioning
• Strong API design and integration skills for AI service orchestration and tool integration
• Knowledge of streaming and async processing for real-time AI applications

Specialized Experience

RAG Systems

• Production experience with document ingestion pipelines, chunking strategies, and metadata management
• Understanding of retrieval quality optimization including re-ranking, query expansion, and context selection
• Experience with multi-modal RAG systems incorporating text, images, and structured data
• Knowledge of RAG evaluation frameworks and automated quality assessment

Agentic Architecture

• Hands-on experience with agent planning algorithms, tool selection mechanisms, and execution engines
• Understanding of multi-agent coordination, communication protocols, and distributed agent systems
• Experience with agent memory systems, state management, and long-running workflow orchestration
• Knowledge of agent safety mechanisms including execution sandboxing and output validation

Preferred Qualifications

• Experience with fine-tuning and RLHF (Reinforcement Learning from Human Feedback) infrastructure
• Knowledge of edge AI deployment and model optimization techniques
• Familiarity with AI governance, compliance frameworks, and ethical AI implementation
• Experience with conversational AI platforms and dialogue management systems
• Understanding of knowledge graphs and symbolic reasoning integration with neural systems

We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally:

• Competitive base salaries 
• Bonus incentives 
• Support for financial-well-being and retirement 
• Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) 
• Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need 
• Generous paid parental leave policies (depending on your location) 
• Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) 
• Free and confidential counseling support through our Healthy Minds program 
• Career development and training opportunities

American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law.  

Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.