Agentic AI Architect - Intelligence Engineering (Central)

Slalom is a business and technology consulting company that co-creates modern technology and software products with clients. The Agentic AI Architect will design and deliver scalable AI/ML and agentic AI solutions, including multi-agent systems, LLM workflows, RAG pipelines, and AI governance frameworks across major cloud platforms. The role also provides technical leadership, mentors engineers, and leads client-facing project delivery.

Responsibilities:

• Provide thought leadership on AI/ML, Generative AI, and Agentic AI internally and with clients, while contributing to a culture of collaboration, learning, and curiosity
• Design end-to-end agentic AI architectures including planning loops, memory management, tool integration, and agent coordination patterns
• Architect multi-agent orchestration systems using frameworks such asStrands Agents SDK,OpenAI Agents SDK, Google ADK, Lang Graph or similar for autonomous reasoning, decision-making, and task execution
• Design and implement Model Context Protocol (MCP) server integrations for tool use, data access, and cross-system interoperability with enterprise systems
• Build advanced retrieval-augmented generation (RAG) systems including vector databases, embedding strategies, chunking optimization, hybrid search, re-ranking, and multi-source data synthesis
• Design and deliver AI and ML solutions across AWS, Azure, and GCP, using the right mix combination of cloud-native data services, ML tooling, LLM platforms, and software engineering practices
• Build in Python and, where useful, other languages to deliver machine learning systems, APIs, evaluation harnesses, retrieval pipelines, agent workflows, and production services
• Recommend and implement architecture for model and agent pipelines, CI/CD, testing, deployment, observability, andMLOps/LLMOps at scale
• Implement evaluation frameworks (e.g., RAGAS, DeepEval, LangSmith) to measure task success rates, tool-call accuracy, and reasoning integrity for GenAI systems
• Build guardrails for safety, compliance, and performance monitoring including human-in-the-loop (HITL) approval workflows, escalation policies, and sandbox isolation
• Define AI governance frameworks including model risk management, responsible AI practices, regulatory compliance, and authorization boundaries for autonomous decision-making
• Explain model and system behavior to both technical and non-technical audiences, including leading deep technical presentations, workshops, and architecture conversations
• Collaborate with Product Owners to apply Slalom’s agile process and lead the initiation, delivery, and transition of projects in a client-facing role
• Lead and mentor engineers and machine learning practitioners. Lead smaller projects (3 to 5 people) as the technical lead from project initiation to delivery
• Build trusted relationships with customers and collaborate across Slalom teams to share learnings and strengthen the broader Intelligence Engineering practice
• Will be delivery-focused approximately 85–95% of the time