AI Solutions Architect

SkillNet Solutions Inc • Austin, TX

2d • Remote

About The Position

SkillNet Solutions, Inc. is a leader in modern commerce, delivering consulting, AI solutions, and technology services to enterprises undergoing digital transformation. By implementing cloud and SaaS applications, SkillNet helps clients adapt to evolving consumer behaviors and build seamless client journeys across B2B, B2C, and B2B2C markets. Since its founding in 1996, SkillNet has partnered with industry leaders such as Oracle, Salesforce, AWS, and others to modernize operations, accelerate agility, and enhance digital and in-store experiences. With solutions delivered across 63 countries for global enterprises including Disney, lululemon athletica, and PayPal, SkillNet continues to redefine what’s possible in unified commerce and retail transformation.

You will work closely with our engineering, product, and architecture teams. Some weeks are whiteboarding sessions and design reviews; others are deep dives into our existing systems. Duties include:

- Reviewing our current AI initiatives with the engineering teams -- understanding what is working, identifying consolidation opportunities, and collaborating on a path toward a unified platform
- Working with engineers and product leads to design the reference architecture for multi-agent orchestration, intent classification and routing (including compound/multi-label intents), and how context flows between agents and sessions
- Collaborating on the context management strategy -- token budgets, conversation summarization, scoped context passing between agents, and the tradeoffs between retrieval and compression
- Designing the RAG architecture together with the data and ML teams -- chunking strategies, hybrid retrieval, reranking, citation grounding, and how batch ingestion and real-time serving fit together
- Helping the team establish prompt governance practices -- versioning, A/B testing, performance monitoring, and rollback workflows
- Defining platform resiliency patterns for LLM-dependent systems -- provider failover, circuit breakers, graceful degradation, cost controls, and observability
- Setting AI safety and governance standards with the team -- guardrails, PII handling, output filtering, and hallucination mitigation
- Partnering with engineering and product leadership to build a sequenced implementation roadmap that our teams can execute against

Requirements

• Designed and shipped multi-agent AI platforms, capable of handling thousands of concurrent sessions with graceful failure modes.
• Built real-time conversational AI systems with proper session memory and context management.
• Architected RAG pipelines beyond prototyping, addressing chunking tradeoffs, embedding drift, stale indexes, and retrieval quality at scale.
• Worked across multiple LLM providers (OpenAI, Claude/Bedrock, Gemini, open-source) and understand their real tradeoffs in cost, latency, quality, and reliability.
• Designed intent classification systems that handle real-world complexity, including multi-label, hierarchical taxonomies, ambiguous inputs, and confidence-based routing to fallbacks or human review.
• Built both real-time and batch ML pipelines and know when to use each, including streaming inference for live interactions, batch processing for catalog-scale operations, and the infrastructure to support both.
• Operated in cloud-native environments (AWS, GCP, or Azure) and can make infrastructure decisions.

Nice To Haves

• Experience in retail, commerce, or customer service AI, understanding domain-specific challenges (product catalogs, order state, returns workflows).
• Hands-on experience with orchestration frameworks (LangGraph, LangChain, LlamaIndex), and understanding their limitations and when to build custom solutions.
• Experience with self-hosted model serving (Ollama, vLLM) for cost optimization or data-sensitive workloads.
• Experience writing AI platform standards adopted by an engineering organization of 50+.

Responsibilities

• Reviewing current AI initiatives with engineering teams to understand what is working, identify consolidation opportunities, and collaborate on a path toward a unified platform.
• Working with engineers and product leads to design the reference architecture for multi-agent orchestration, intent classification and routing (including compound/multi-label intents), and context flow between agents and sessions.
• Collaborating on the context management strategy, including token budgets, conversation summarization, scoped context passing between agents, and tradeoffs between retrieval and compression.
• Designing the RAG architecture with data and ML teams, covering chunking strategies, hybrid retrieval, reranking, citation grounding, and the integration of batch ingestion and real-time serving.
• Establishing prompt governance practices, including versioning, A/B testing, performance monitoring, and rollback workflows.
• Defining platform resiliency patterns for LLM-dependent systems, such as provider failover, circuit breakers, graceful degradation, cost controls, and observability.
• Setting AI safety and governance standards with the team, including guardrails, PII handling, output filtering, and hallucination mitigation.
• Partnering with engineering and product leadership to build a sequenced implementation roadmap for execution by the teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed