Agentic AI / Machine Learning Architect - Senior Principal

Who You’ll Work With

At Slalom, we co-create modern technology and software products with clients who are accelerating their digital transformation journeys. We blend design, product engineering, analytics, automation, and AI-native delivery to build intelligent products and platforms that can operate safely at enterprise scale. As an AI/ML Architect, you’ll design and deliver production-grade AI systems that combine machine learning, generative AI, agentic workflows, modern data platforms, and cloud-native engineering across AWS, Azure, and Google Cloud. You’ll partner with clients to shape strategy, define secure and governed architectures, and move AI solutions from experimentation into reliable business operations.

What You’ll Do

• Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
• Define secure, scalable, cloud-native and hybrid reference architectures across AWS, Azure, and Google Cloud, including modern AI platform services such as Amazon Bedrock, Azure AI Foundry, Google Vertex AI, and enterprise data platforms.
• Guide applied AI strategy and delivery across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
• Lead enterprise adoption of production GenAI and agentic AI, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
• Establish AI evaluation, observability, and reliability standards, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
• Champion Responsible AI, AI security, and governance-by-design practices, including explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance alignment.
• Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing executive-ready recommendations based on use case fit, enterprise readiness, cost, risk, and operational complexity.
• Lead and mentor cross-functional delivery teams of data engineers, AI engineers, ML engineers, software engineers, architects, and consultants, ensuring consistent quality across complex programs.
• Drive business development through proposals, executive client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
• Develop senior practitioners and practice capability, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.
• Help to hire, lead, mentor, and retain a high-performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.

What You’ll Bring

• 9+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
• 5+ years of experience in professional consulting or IT services, with proven ability to lead complex client-facing technical engagements.
• Proven ability to design and govern production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, operating model, and user experience into an end-to-end enterprise architecture.
• Deep expertise in modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, re-ranking, knowledge graphs, tool/function calling, structured outputs, context engineering, and multimodal inputs.
• Experience defining agentic AI architecture patterns, including single-agent and multi-agent workflows, supervisor/worker patterns, state and memory management, workflow orchestration, human approval gates, and safe action execution.
• Proficiency with modern AI engineering frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, Hugging Face, or comparable open-source and cloud-native frameworks.
• Strong programming skills in Python and modern software engineering practices, with familiarity in APIs, event-driven patter…