AI Architect

Role Type: AI Architect

Preferred Start Date : ASAP

Duration : 5 months

Telecommute : Yes

Description:

Summary:

We are seeking an experienced AI Solution Architect to lead the design and implementation of cutting-edge AI solutions. This is a hands-on technical leadership role responsible for end-to-end architecture, guiding development teams, and ensuring production-quality outcomes. The ideal candidate will combine deep technical expertise with

Key Responsibilities

• Architectural Design: Develop comprehensive AI system architecture covering data ingestion, storage, retrieval, and conversational interfaces powered by RAG (Retrieval-Augmented Generation).
• Technology Selection: Evaluate and implement vector databases, embedding strategies, and LLM orchestration frameworks.
• Data Pipeline Engineering: Design incremental and event-driven pipelines for structured and unstructured data.
• Advanced Retrieval Systems: Build solutions supporting both structured queries and natural language questions, including temporal intelligence for historical comparisons and trend analysis.
• Agentic AI Solutions: Design and implement agentic systems capable of tool use, multi-step reasoning, and autonomous workflows.
• Agile Leadership: Lead sprint planning, assign tasks, coordinate with project managers, and unblock developers.
• Client Coordination: Act as the primary technical liaison with clients to gather requirements, provide updates, and ensure alignment with business objectives.
• Compliance & Security: Ensure all solutions adhere to organizational compliance, privacy, and regulatory standards.
• Quality Assurance: Define and monitor accuracy benchmarks, performance metrics, and system reliability.

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Required Qualifications

• 4+ years of experience designing and deploying production-grade RAG systems.
• Hands-on expertise with vector databases (e.g., Pinecone, Weaviate, Qdrant, pgvector).
• Strong proficiency in LLM orchestration frameworks (LangChain, LlamaIndex, Semantic Kernel, or custom solutions).
• Cloud deployment experience (AWS, Azure, or Google Cloud Platform).
• Proven ability to handle both structured and unstructured data in AI contexts.
• Demonstrated success in leading technical teams through agile delivery.
• Strong client-facing communication and stakeholder management skills.
• Knowledge of compliance frameworks and secure architecture principles.