What are the responsibilities and job description for the AI Solutions Architect position at SkillNet Solutions Inc?
Title: AI Solutions Architect
Type: Contract / Consulting
Duration: 6 months (extendable)
Location: Remote (US time zone overlap required)
Experience: 10 years in software/ML architecture, 5 years in enterprise AI
About SkillNet Solutions:
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.
Job Summary:
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
Experience:
This is not a wish list. These are the things you will be doing in week one. If you have not done them in production, this is not the right engagement.
- Designed and shipped multi-agent AI platforms -- you know the difference between a demo and a system that handles thousands of concurrent sessions with graceful failure modes
- Built real-time conversational AI systems with proper session memory and context management -- not just chat wrappers around an LLM API
- Architected RAG pipelines that went beyond prototyping -- you have dealt with chunking tradeoffs, embedding drift, stale indexes, and retrieval quality at scale
- Worked across multiple LLM providers (OpenAI, Claude/Bedrock, Gemini, open-source) and understand the real tradeoffs in cost, latency, quality, and reliability -- not just benchmark scores
- Designed intent classification systems that handle real-world complexity -- 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 which -- 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, not just architecture diagrams
Preferred Skills/Experience:
- Experience in retail, commerce, or customer service AI -- you understand the domain-specific challenges (product catalogs, order state, returns workflows)
- Hands-on with orchestration frameworks (LangGraph, LangChain, LlamaIndex) -- but more importantly, you know their limitations and when to build custom
- Experience with self-hosted model serving (Ollama, vLLM) for cost optimization or data-sensitive workloads
- Have been the person who wrote the AI platform standards that an engineering org of 50 adopted
What We Will Build Together
Over the course of the engagement, you will collaborate with our teams to produce the following artifacts that will guide our platform buildout:
- AI Platform Reference Architecture with decision rationale
- Multi-Agent Orchestration & Context Management Strategy
- Intent Routing Framework with classification taxonomy
- RAG Architecture covering ingestion, retrieval, and serving layers
- Prompt Governance Standards & Tooling Recommendations
- Platform Resiliency & Observability Design
- Sequenced Implementation Roadmap
Type: Contract / Consulting
Duration: 6 months (extendable)
Location: Remote (US time zone overlap required)
Experience: 10 years in software/ML architecture, 5 years in enterprise AI
About SkillNet Solutions:
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.
Job Summary:
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
Experience:
This is not a wish list. These are the things you will be doing in week one. If you have not done them in production, this is not the right engagement.
- Designed and shipped multi-agent AI platforms -- you know the difference between a demo and a system that handles thousands of concurrent sessions with graceful failure modes
- Built real-time conversational AI systems with proper session memory and context management -- not just chat wrappers around an LLM API
- Architected RAG pipelines that went beyond prototyping -- you have dealt with chunking tradeoffs, embedding drift, stale indexes, and retrieval quality at scale
- Worked across multiple LLM providers (OpenAI, Claude/Bedrock, Gemini, open-source) and understand the real tradeoffs in cost, latency, quality, and reliability -- not just benchmark scores
- Designed intent classification systems that handle real-world complexity -- 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 which -- 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, not just architecture diagrams
Preferred Skills/Experience:
- Experience in retail, commerce, or customer service AI -- you understand the domain-specific challenges (product catalogs, order state, returns workflows)
- Hands-on with orchestration frameworks (LangGraph, LangChain, LlamaIndex) -- but more importantly, you know their limitations and when to build custom
- Experience with self-hosted model serving (Ollama, vLLM) for cost optimization or data-sensitive workloads
- Have been the person who wrote the AI platform standards that an engineering org of 50 adopted
What We Will Build Together
Over the course of the engagement, you will collaborate with our teams to produce the following artifacts that will guide our platform buildout:
- AI Platform Reference Architecture with decision rationale
- Multi-Agent Orchestration & Context Management Strategy
- Intent Routing Framework with classification taxonomy
- RAG Architecture covering ingestion, retrieval, and serving layers
- Prompt Governance Standards & Tooling Recommendations
- Platform Resiliency & Observability Design
- Sequenced Implementation Roadmap