The Human Capital Offering Portfolio focuses on helping organizations manage and sustain their performance through their most important asset: their people. Centered on five core issues, this Portfolio signifies to the market that we see Human Capital as a topic critical to the C-Suite. As we go-to-market, we will show our clients that we serve more than HR organizations – from the CEO to CFO, Risk Manager to Business Unit leader—and that we deliver on our issues and help create value for our clients.
Insights, Innovation & Operate Offering:
Our Insights, Innovation & Operate Offering is designed to enhance key aspects of our clients’ businesses by leveraging cutting-edge technology, data, and a blend of deep technical and human expertise. We innovate and deliver creative, industry-specific solutions that streamline operations and accelerate speed-to-value.
Position Summary: AI Solutions Architect (Senior Consultants, Managers)
We are seeking an AI Solutions Architect to design and deliver production-grade AI agent platforms. The core of this role is building multi-agent orchestration systems using LangGraph and Anthropic Claude—routing user intent to specialized sub-agents, implementing retrieval-augmented generation (RAG) over policy documents, and performing actions. You will own the architecture from LLM prompt design to multi-tenancy and vendor integration across HR platforms (Workday, SAP, Oracle HCM). You will collaborate with engineering, security, and product teams to translate business requirements into production-ready AI agent solutions.
Experience bands:
-
Senior Consultant: 6+ years total experience, with significant time in AI/ML solution design
-
Manager: 10+ years total experience, with significant time in AI/ML solution design
-
Design and implement multi-agent orchestration using LangGraph—intent classification, sub-agent routing, state management, checkpointing, and human-in-the-loop (HITL) interrupt flows.
-
Architect RAG pipelines: document ingestion, chunking strategies, embedding generation, vector search (pgvector), hybrid retrieval, and citation-grounded response synthesis.
-
Evaluate and integrate LLM providers (Anthropic Claude, OpenAI, etc.), defining model selection strategies, prompt engineering standards, and token budget management across agent tiers and multi-tenant data layers
-
Design vendor integration patterns using direct APIs, MCP, and A2A protocol for complex HR platform workflows.
-
Define end-to-end AI solution architectures from user interaction through agent orchestration, vendor API integration, and production deployment with monitoring.
-
, own the AI application architecture (agent orchestration, LLM integration, RAG, guardrails, eval frameworks) in joint partnership with the Cloud Solution Architect, who owns the cloud platform and infrastructure layer.
-
, partner with the Data Integration Architect who owns document ingestion and vector store architecture; own downstream AI application concerns (retrieval strategy, prompt design, response synthesis, evaluation).
-
Conduct architecture reviews and mentor teams on best practices in LLM application design, agent patterns, and production readiness.
-
Collaborate with stakeholders to translate requirements into architecture decisions and delivery roadmaps; produce HLD/LLD and ADRs.
-
6+ years (Senior Consultant) or 10+ years (Manager) total experience, with significant time in AI/ML solution design and implementation.
-
Hands-on proficiency with agent orchestration frameworks - LangGraph strongly preferred.
-
Strong understanding of retrieval-augmented generation
-
Experience with prompt engineering, tool calling, and LLM evaluation techniques
- Expert-level Python development for production systems
- Strong hands-on experience building API services with FastAPI
- Working knowledge of multi-tenant security patterns
-
(Claude Code, GitHub Copilot, Cursor, Windsurf, or equivalent) for architecture work, design documentation, and engineering workflows. Familiarity with agentic engineering patterns is expected.
-
Strong communication and documentation skills; ability to influence cross-functional teams and senior stakeholders.
-
Familiarity with the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol for vendor and cross-system integrations.
-
Experience integrating with HR/HCM platforms
- Experience with LangFuse, LangSmith, or similar LLM observability and tracing platforms.
-
Knowledge of responsible AI practices: PII handling, prompt injection defense, model risk management, and explainability.
-
— working familiarity with cloud-native deployment (Kubernetes, CI/CD, IaC, observability), data engineering patterns (ETL/ELT, lakehouse, streaming), and the operational implications for AI platforms. Not expected to own these, but must collaborate effectively with Cloud Solution and Data Integration architects in joint reviews.
-
Architecture leadership with pragmatic trade-off decisions (speed vs. risk, cost vs. accuracy, build vs. buy, direct integration vs. protocol abstraction).
-
Ability to bridge AI research and engineering - turning prototypes into reliable, observable production services.
-
Strong ownership mindset for reliability, security, and operational excellence.
-
Coaching and standard-setting across teams.
Job Description – AI Solution Architect
The Human Capital Offering Portfolio focuses on helping organizations manage and sustain their performance through their most important asset: their people. Centered on five core issues, this Portfolio signifies to the market that we see Human Capital as a topic critical to the C-Suite. As we go-to-market, we will show our clients that we serve more than HR organizations – from the CEO to CFO, Risk Manager to Business Unit leader—and that we deliver on our issues and help create value for our clients.
Insights, Innovation & Operate Offering:
Our Insights, Innovation & Operate Offering is designed to enhance key aspects of our clients’ businesses by leveraging cutting-edge technology, data, and a blend of deep technical and human expertise. We innovate and deliver creative, industry-specific solutions that streamline operations and accelerate speed-to-value.
Position Summary: AI Solutions Architect (Senior Consultants, Managers)
Role Summary
We are seeking an AI Solutions Architect to design and deliver production-grade AI agent platforms. The core of this role is building multi-agent orchestration systems using LangGraph and Anthropic Claude—routing user intent to specialized sub-agents, implementing retrieval-augmented generation (RAG) over policy documents, and performing actions. You will own the architecture from LLM prompt design to multi-tenancy and vendor integration across HR platforms (Workday, SAP, Oracle HCM). You will collaborate with engineering, security, and product teams to translate business requirements into production-ready AI agent solutions.
Experience bands:
-
Senior Consultant: 6+ years total experience, with significant time in AI/ML solution design
-
Manager: 10+ years total experience, with significant time in AI/ML solution design
Key Responsibilities
-
Design and implement multi-agent orchestration using LangGraph—intent classification, sub-agent routing, state management, checkpointing, and human-in-the-loop (HITL) interrupt flows.
-
Architect RAG pipelines: document ingestion, chunking strategies, embedding generation, vector search (pgvector), hybrid retrieval, and citation-grounded response synthesis.
-
Evaluate and integrate LLM providers (Anthropic Claude, OpenAI, etc.), defining model selection strategies, prompt engineering standards, and token budget management across agent tiers and multi-tenant data layers
-
Design vendor integration patterns using direct APIs, MCP, and A2A protocol for complex HR platform workflows.
-
Define end-to-end AI solution architectures from user interaction through agent orchestration, vendor API integration, and production deployment with monitoring.
-
, own the AI application architecture (agent orchestration, LLM integration, RAG, guardrails, eval frameworks) in joint partnership with the Cloud Solution Architect, who owns the cloud platform and infrastructure layer.
-
, partner with the Data Integration Architect who owns document ingestion and vector store architecture; own downstream AI application concerns (retrieval strategy, prompt design, response synthesis, evaluation).
-
Conduct architecture reviews and mentor teams on best practices in LLM application design, agent patterns, and production readiness.
-
Collaborate with stakeholders to translate requirements into architecture decisions and delivery roadmaps; produce HLD/LLD and ADRs.
Required Qualifications
-
6+ years (Senior Consultant) or 10+ years (Manager) total experience, with significant time in AI/ML solution design and implementation.
-
Hands-on proficiency with agent orchestration frameworks - LangGraph strongly preferred.
-
Strong understanding of retrieval-augmented generation
-
Experience with prompt engineering, tool calling, and LLM evaluation techniques
-
Expert-level Python development for production systems
-
Strong hands-on experience building API services with FastAPI
-
Working knowledge of multi-tenant security patterns
-
(Claude Code, GitHub Copilot, Cursor, Windsurf, or equivalent) for architecture work, design documentation, and engineering workflows. Familiarity with agentic engineering patterns is expected.
-
Strong communication and documentation skills; ability to influence cross-functional teams and senior stakeholders.
Preferred Qualifications
-
Familiarity with the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocol for vendor and cross-system integrations.
-
Experience integrating with HR/HCM platforms
-
Experience with LangFuse, LangSmith, or similar LLM observability and tracing platforms.
-
Knowledge of responsible AI practices: PII handling, prompt injection defense, model risk management, and explainability.
-
— working familiarity with cloud-native deployment (Kubernetes, CI/CD, IaC, observability), data engineering patterns (ETL/ELT, lakehouse, streaming), and the operational implications for AI platforms. Not expected to own these, but must collaborate effectively with Cloud Solution and Data Integration architects in joint reviews.
Key Competencies
-
Architecture leadership with pragmatic trade-off decisions (speed vs. risk, cost vs. accuracy, build vs. buy, direct integration vs. protocol abstraction).
-
Ability to bridge AI research and engineering - turning prototypes into reliable, observable production services.
-
Strong ownership mindset for reliability, security, and operational excellence.
-
Coaching and standard-setting across teams.
Education
Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field (or equivalent practical experience). Advanced degree is a plus.
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