AI Solution Architect

Photon; Boston (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

Photon is seeking an Enterprise AI Architect to define, design, and govern end-to-end Generative AI strategies across a global enterprise. You will establish reference architectures for multi-agent systems, enable secure tool usage, and drive scalable RAG pipelines with offshore/onshore collaboration.

The role requires 10–15+ years of software engineering and 4+ years in AI/ML architecture, with hands-on experience in LLMs, vector databases, and cloud AI platforms.

Qualifications

• 10–15+ years of software engineering experience, including 4+ years leading AI/ML architecture and complex system design.
• Production experience architecting and scaling Agentic AI systems, Multi-Agent Workflows, and Enterprise RAG pipelines.

Responsibilities

• Define enterprise reference architectures for multi-agent systems, agentic orchestration, distributed memory, and advanced hybrid RAG pipelines.
• Drive technology evaluation, selection, and trade-off analysis across LLMs, vector databases, orchestration frameworks, and cloud AI platforms.
• Design multi-tenant, enterprise-scale AI platforms supporting agent-to-agent communication, secure tool use, and long-running asynchronous workflows.
• Lead total cost of ownership, latency, and token optimization strategies across all business units.
• Establish enterprise-wide AI governance, security, and safety blueprints (PII masking, red teaming, prompt injection protection, dynamic access controls).
• Standardize LLMOps and Observability platforms—defining metrics for evaluation, tracing, drift monitoring, hallucination prevention, and unit costs.

Job description

Enterprise AI Architect (Agentic Systems & Generative AI)

Experience: 10–15+ years overall engineering experience, with 4+ years dedicated to AI/ML & LLM architecture

Primary Objective

We are seeking an Enterprise AI Architect to define, design, and govern our end-to-end Generative AI, multi-agent system, and enterprise RAG strategy. In this strategic technical leadership role, you will define the reference architectures, toolings, evaluation frameworks, and guardrails necessary to scale production-grade AI agents, intelligent copilots, and knowledge platforms across the global enterprise.

Success looks like: Scalable reference architectures, low total cost of ownership (TCO) model selection, robust AI governance and guardrail frameworks, and seamless enterprise integration blueprints executed reliably across onshore/offshore teams.

Key Responsibilities

Architectural Leadership & Strategy

Define enterprise reference architectures for multi-agent systems, agentic orchestration, distributed memory, and advanced hybrid RAG pipelines.

Drive technology evaluation, selection, and trade-off analysis across LLMs (proprietary vs. open-source), vector databases, orchestration frameworks (LangChain/LangGraph, AutoGen, CrewAI), and cloud AI platforms.

Design multi-tenant, enterprise-scale AI platforms supporting agent-to-agent communication, secure tool use, and long-running asynchronous workflows.

Lead total cost of ownership (TCO), latency, and token optimization strategies across all business units.

Governance, Security & Operational Blueprinting

Establish enterprise-wide AI governance, security, and safety blueprints (PII masking, red teaming, prompt injection protection, dynamic access controls).

Standardize LLMOps and Observability platforms—defining org-wide metrics for evaluation, tracing, drift monitoring, hallucination prevention, and unit costs.

Architect seamless integration patterns for core enterprise systems (Salesforce, SharePoint, Confluence, ERP/CRM systems, SQL/NoSQL databases) into agentic platforms.

Cross-Functional & Delivery Guidance

Partner with Senior Agentic AI Engineers, Data Scientists, Security Officers, and Business Executives to translate high-level business drivers into technical blueprints.

Establish development standards, design patterns, code review guidelines, and best practices for global offshore/onshore engineering groups.

Technical Experience & Qualifications

Must-Have Experience & Skills

10–15+ years of software engineering experience, including 4+ years leading AI/ML architecture and complex system design.

Production experience architecting and scaling Agentic AI systems, Multi-Agent Workflows, and Enterprise RAG pipelines using Python, LangChain/LangGraph, LlamaIndex, or native SDKs.

Deep knowledge of LLM internals, prompt engineering strategies (CoT, ReAct, Reflexion), fine-tuning vs. RAG trade-offs, and embedding space dynamics.

Extensive experience with cloud AI infrastructure across at least one major platform (Azure OpenAI, AWS Bedrock, or Google Vertex AI).

Hands-on expertise with vector search ecosystems (Pinecone, Qdrant, OpenSearch, pgvector) and hybrid search/reranking architectures.

Proven track record architecting secure API services, event-driven architectures (Kafka/RabbitMQ), and asynchronous microservices.

Preferred Skills

Hands-on expertise in enterprise security frameworks, IAM, RBAC, and data privacy regulations (SOC2, GDPR, HIPAA) as applied to GenAI.

Strong background in traditional ML, NLP, continuous evaluation frameworks (Ragas, TruLens, DeepEval), and CI/CD for LLMs.

Container orchestration and infrastructure-as-code (Docker, Kubernetes, Terraform).

Experience implementing agentic memory layers (Redis, graph databases, episodic/semantic memory stores).

Core Competencies & Soft Skills

Architectural Vision: Ability to balance cutting-edge GenAI innovation with enterprise stability, cost-efficiency, and risk mitigation.

Executive Communication: Skill in translating complex technical concepts into clear strategic roadmaps for executive stakeholders.

Technical Mentorship: Demonstrated ability to guide, upskill, and review the architectural output of distributed senior engineering teams.