Solutions Architect – Agentic AI
Core
Design, develop, and deploy scalable agentic AI solutions for enterprise environments using LLMs, RAG, and AI agents.
Role type
Senior Solutions Architect (Agentic AI)
Builds
Scalable, secure, and maintainable agentic AI systems and workflows for enterprise clients.
Domain
Enterprise Software / Generative AI
Deliverable
production ML models
Required skills
LLM platforms (Azure OpenAI, OpenAI, Anthropic, AWS Bedrock), Agentic frameworks (LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen), RAG pipelines (vector databases, embeddings, hybrid search), Python, Cloud infrastructure (Azure, AWS, GCP), Kubernetes, LLMOps, API design (REST, GraphQL), CI/CD, Terraform/Bicep, LLM evaluation frameworks (Ragas, DeepEval)
Preferred skills
C#/.NET or TypeScript, Model fine-tuning, Prompt engineering, Microservices architecture
Technologies
Azure OpenAI, OpenAI, Anthropic, AWS Bedrock, Google Vertex AI, LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, AutoGen, Pinecone, Weaviate, Qdrant, pgvector, Azure AI Search, Python, FastAPI, ASP.NET Core, Docker, Kubernetes, Azure Functions, AWS Lambda, Azure DevOps, GitHub Actions, Terraform, Bicep, Ragas, DeepEval, LangSmith, Langfuse
Responsibilities
Lead end-to-end architecture of agentic AI solutions combining LLMs, RAG, and agents; Design and deploy AI applications and agent workflows; Integrate AI components with enterprise APIs and data platforms; Define evaluation frameworks for LLM and agent outputs; Drive performance and cost optimization through prompt engineering and model routing; Own production health of AI workloads through LLMOps practices; Provide technical guidance and mentor engineers.
Seniority
Senior, hands-on IC with leadership
