About the Role

This role sits at the intersection of GenAI architecture, product thinking, and real-world delivery.

As a GenAI Solutions Architect, you’ll design and own agentic, LLM-powered systems from first principles to production. This is not a slideware or advisory role—you’ll shape architectures that actually ship, scale, and survive real enterprise usage.

You’ll work closely with product, customers, and engineering to turn ambiguous business problems into reliable, safe, and scalable GenAI systems.

What You’ll Do

Design end-to-end GenAI and agentic architectures (multi-agent workflows, tools, memory, RAG)
Translate business problems into clear GenAI solution blueprints (models, orchestration, data flow, integrations)
Define best practices for prompting, tool-calling, context management, and long-running workflows
Architect RAG and knowledge pipelines (vector stores, embeddings, indexing strategies)
Evaluate and recommend LLMs and model providers (OpenAI, Azure OpenAI, Bedrock, open-source)
Define evaluation frameworks: quality metrics, guardrails, safety checks, human-in-the-loop
Partner with engineering to implement agent-based systems (Python-first, Lyzr platform)
Design APIs and integration patterns across agents, microservices, and external systems
Drive non-functional requirements: latency, scalability, observability, cost, resilience
Work directly with product teams and customers to define scope, risks, and trade-offs
Own architecture documentation, decision records, and solution diagrams
Ensure security, privacy, and compliance for AI systems (PII, access control, data handling)
Define monitoring and observability for GenAI agents (logging, tracing, drift, hallucinations)

What You Need

6+ years in software / solution architecture or backend engineering
1–2+ years hands-on building and shipping GenAI systems
Proven experience delivering:

• RAG systems

• Agent / chat / copilot workflows

• Document or workflow automation

Strong understanding of:

• LLMs, embeddings, prompting, tool-calling

• Vector databases (Qdrant, Weaviate, Chroma, PGVector)

• System design and API architecture

• Cloud experience (AWS required; Azure/GCP a plus)

• Ability to balance experimentation with production rigor (cost, performance, safety)

• Strong ownership, product mindset, and customer-facing comfort

• Clear communicator who can explain GenAI trade-offs to mixed audiences

Nice to Have

Startup or zero-to-one product experience
Exposure to AI/ML product ecosystems
Experience designing GenAI systems for enterprise customers

Why Lyzr

Architect real agentic systems, not demos or experiments
Work on GenAI problems that matter at enterprise and platform scale
High ownership, direct customer impact, and real architectural influence
Build on a platform shaping how developers adopt agentic AI in production