Senior AI Architect for Enterprise GenAI & MLOps

Slalom; St. Louis (MO); On-site; USD 198,000 - 247,000; Full time

Benefits offered by this job

401(k) with match

Healthcare coverage (health, dental, &

Vision coverage

Job summary

Slalom is seeking an AI/ML Architect to design and deliver production-grade AI systems across AWS, Azure, and Google Cloud. You’ll partner with clients to shape strategy, define secure architectures, and move AI solutions from experimentation into reliable business operations.

Lead cross-functional delivery teams, guide GenAI initiatives, establish MLOps standards, and champion responsible AI while driving enterprise adoption of agentic AI.

Qualifications

• 9+ years of experience implementing ML/AI solutions in production.
• 5+ years of experience in professional consulting or IT services.
• Proven ability to design and govern production AI systems across models, data, retrieval, orchestration, APIs, security, observability, and user experience.

Responsibilities

• Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, and lifecycle management.
• Define secure, scalable cloud-native architectures across AWS, Azure, and Google Cloud.
• Lead enterprise adoption of production GenAI and agentic AI with advanced RAG, tool calling, and human-in-the-loop controls.
• Establish AI evaluation, observability, and reliability standards including testing, tracing, and cost monitoring.
• Champion Responsible AI, privacy, bias mitigation, and governance-by-design practices.

Skills

AI architecture

Python

GenAI

Cloud platforms

Leadership

Stakeholder communication

MLOps

Data platform knowledge

Tools

LangChain

LangGraph

LlamaIndex

Semantic Kernel

Haystack

CrewAI

Hugging Face

Job description

Slalom is seeking an AI/ML Architect to design and deliver production-grade AI systems across AWS, Azure, and Google Cloud. You’ll partner with clients to shape strategy, define secure architectures, and move AI solutions from experimentation into reliable business operations.

Lead cross-functional delivery teams, guide GenAI initiatives, establish MLOps standards, and champion responsible AI while driving enterprise adoption of agentic AI.