Note: The job is a remote job and is open to candidates in USA. Accelerize 360 is a company focused on AI solutions, and they are seeking an AI Architect to lead the technical design and implementation of AI systems. The role involves working closely with clients to translate business requirements into technical specifications, ensuring the delivery of robust AI architectures that meet client needs.

Responsibilities

• Design end-to-end AI solution architectures: model selection, data pipelines, orchestration layers, integration points, and deployment infrastructure
• Build rapid prototypes that make AI concepts tangible for clients - fast enough to drive decisions, rigorous enough to inform the real build
• Translate business requirements and AI strategy into implementation-ready technical specifications
• Evaluate and recommend the right components of the AI stack - LLMs, vector databases, fine-tuning approaches, RAG patterns, agents, APIs - based on the client's constraints and goals
• Define and enforce architecture standards across the delivery team; catch design mistakes before they become production problems
• Lead technical discovery with clients: assess existing data infrastructure, identify gaps, and size the build effort honestly
• Act as the senior technical voice in client-facing conversations - not just capable of communicating complexity clearly, but expected to do so
• Review and guide the work of AI engineers; flag architectural drift early and course-correct without creating bottlenecks
• Contribute to internal IP: reusable patterns, accelerators, and architecture frameworks that raise the floor across all engagements

Skills

• 7+ years in software or data engineering, with at least 3 years in a hands-on architecture role
• Consulting or professional services background is required - you understand what it means to deliver under a fixed timeline with a client watching
• Deep fluency in designing and deploying production AI/ML systems - not just familiarity with the concepts
• Practical experience with LLM application patterns: RAG, agents, function calling, prompt engineering at scale, evaluation frameworks
• Strong command of at least one major cloud platform (AWS, Azure, or GCP) and its AI/ML services - SageMaker, Azure ML, or Vertex AI - and the ability to architect for cost, latency, and reliability simultaneously
• Hands-on experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or similar; vector databases such as Pinecone, Weaviate, or pgvector; and model serving infrastructure
• Proficiency in Python and comfort across the modern data stack: dbt, Airflow or similar orchestration, Snowflake or equivalent cloud data platforms
• Ability to write and review code - you don't need to be the fastest engineer in the room, but you need to read it fluently and know when something is wrong
• Comfort operating in ambiguous, client-facing environments where requirements evolve and trade-offs are constant

Company Overview

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