Architect GenAI Solutions: Design end-to-end architectures for AI-powered applications
from LLM selection and prompt engineering through orchestration, retrieval-augmented generation (RAG), and integration with enterprise data environments.
Build and Orchestrate Agents: Go beyond design
build agentic AI workflows using frameworks like LangChain, LangGraph, AutoGen, CrewAI, or similar. You know how to orchestrate multi-agent systems and have shipped something real.
Own the AI Stack: Work across the full GenAI delivery stack: LLM APIs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock), vector databases, RAG pipelines, evaluation frameworks, and deployment infrastructure.
Bridge to Enterprise Systems: Integrate GenAI solutions with existing enterprise architecture
data platforms, APIs, identity and access controls, and compliance requirements. Make AI work within the constraints of real organizations.
Advise Client Leadership: Translate technical architecture decisions into clear strategic narratives for client leadership. Help clients move from AI experimentation to AI-enabled operations.
Accelerate the Team: Bring patterns, reusable components, and technical standards to the delivery team. Be the person who makes everyone else faster.

Hands-On GenAI Experience: You have built production or near-production GenAI systems
not just demos. You've wrestled with evaluation, hallucination, latency, and cost at the architecture level.
Agentic AI Fluency: Practical experience building agentic workflows and multi-agent systems using frameworks like LangChain, LangGraph, AutoGen, CrewAI, or similar. You understand where these tools shine and where they fall apart.
LLM Platform Breadth: Hands-on experience with major LLM platforms and APIs: OpenAI, Anthropic Claude, Azure OpenAI, AWS Bedrock, or equivalent. You know the trade-offs between them.
Engineering Foundation: Strong Python skills and comfort in cloud-native environments (AWS, Azure, or GCP). You can write the code, not just spec it.
Data Architecture Context: Understanding of how AI systems consume data
vector stores, embeddings, RAG architectures, and the data infrastructure that sits underneath production AI applications.
Consulting & Communication: Experience working in client-facing or consulting delivery contexts. You can navigate organizational complexity and explain AI architecture to both technical teams and senior business stakeholders.
Builder Mentality: You default to building. You use AI tools to accelerate your own work. You're excited to operate at the frontier of what's actually deployable in enterprise environments
not just what's theoretically possible.

Solution Architect – GenAI

Key skills