AI/ML Architect - Principal (US - EAST)

AI/ML Architect – Principal

**** Please note: This role is not eligible for 100% remote work. Employees must live within a commutable distance of a Slalom Office and must be willing to be onsite at the client and/or Slalom office when needed up to 3 days a week. ****

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

• Architect and deliver enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
• Design secure, scalable, cloud-native and hybrid 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.
• Lead applied AI solution design across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
• Enable production GenAI and agentic AI adoption, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
• Define AI evaluation, observability, and reliability patterns, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
• Champion Responsible AI and AI security practices, including governance, explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance-by-design.
• Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing practical 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 on-time, high-quality outcomes.
• Support business development through proposals, client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
• Coach and mentor junior consultants, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.

What You’ll Bring

• 6+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.

• 3+ years of experience in professional consulting or IT services, with proven ability to lead client-facing technical engagements.

• Hands-on experience designing production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, and user experience into an end-to-end architecture.
• Deep understanding of 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 with 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 patterns, test automation, infrastructure as code, and scalable service design.
• Proficiency in cloud AI/ML platforms…