Principle AI Architect

Role Overview

We are seeking a highly experienced Principal AI Architect with strong hands-on engineering expertise in modern AI systems including LLMs agentic workflows and enterprise-scale integrations. This role involves working closely with clients to understand complex business problems and translating them into scalable production-ready AI architectures. The ideal candidate combines deep technical expertise with architectural thinking to design build and guide the development of intelligent systems.

Key Responsibilities

Engage with clients and stakeholders to understand business challenges and define structured problem statements.

Translate business requirements into scalable technically feasible AI architectures.

Design end-to-end AI systems including data ingestion retrieval reasoning and orchestration layers. Architect and implement Agentic RAG (Retrieval-Augmented Generation) systems.

Design and develop multi-agent systems using patterns such as planner executor architectures.

Build and orchestrate AI-driven workflows for complex enterprise use cases.

Design and implement stateful agent systems including memory architectures (short-term and long-term memory).

Integrate vector databases (ChromaDB Milvus PGVector) for semantic search and retrieval.

Develop and integrate knowledge graphs to enhance contextual reasoning.

Implement LLM guardrails and evaluation frameworks (e.g. RAGAS DeepEval) to ensure reliability and performance.

Design data ingestion pipelines for structured and unstructured enterprise data sources.

Build modular maintainable systems using SOLID principles and best software engineering practices.

Develop and deploy AI services using microservices-based architectures and APIs.

Conduct feasibility studies and architectural analysis forintegrating AI with legacy systems (e.g. SAP ecosystems).

Create detailed technical specifications architecture diagrams and design documentation.

Implement observability and tracing using tools like LangSmith for debugging and monitoring AI workflows.

Mentorjunior engineers and provide technical leadership across AI initiatives.

Required Skills s Expertise
Strong proficiency in Python programming.

Deep experience with LLM systems prompt engineering and agentic architectures.

Hands-on expertise in Agentic RAG systems and retrieval pipelines.

Experience with vector databases (ChromaDB Milvus PGVector).

Knowledge of LLM evaluation and guardrails frameworks (RAGAS DeepEval).

Experience designing multi-agent systems and AI workflows.

Strong understanding of state management and memory systems in AI agents.

Experience with LangChain ecosystem and observability tools (LangSmith).

Solid understanding of microservices architecture and API-driven systems.

Experience designing scalable AI architectures for enterprise environments.

Familiarity with knowledge graph design and integration.

Strong foundation in Machine Learning and Deep Learning concepts.

Experience working with AWS and Docker.

Strong problem-solving and analyticalthinking abilities.

Excellent communication skills for clientinteraction and technical documentation.

Preferred Qualifications

Experience deploying AI systems in production environments at scale.

Exposure to enterprise-grade AI system design (security scalability cost considerations).

Familiarity with distributed systems and cloud-native architectures.

Prior experience mentoring orleading engineering teams.