Senior AI Solutions Architect

Position: Senior AI Solutions Architect
Location: Onsite (Denver, CO)

Duration: Long Term Contract
Client: Charter Communications

Onsite Interview.

Mission:

Drive adoption of AI/GenAI across the organization by translating business problems into scalable AI solutions and ensuring delivery from idea → production.

Core Responsibilities:

· Define AI / GenAI strategy & roadmap

· Identify high-value use cases (automation, copilots, analytics, etc.) 

· Translate business needs → AI solution design (RAG, agents, ML models)

· Lead cross-functional execution (engineering, data, product, design) 

· Own end-to-end lifecycle (discovery → build → deploy → optimize) 

· Establish AI governance, standards, and best practices

· Track impact (ROI, adoption, performance metrics) 

Primary Skills (Must-Have):

These were the “non-negotiables”:

1. AI / GenAI Expertise

· LLMs, RAG, copilots, NLP 

· Prompt engineering basics 

· Understanding of ML lifecycle 

2. Product + Strategy Thinking

· Strong product management mindset 

· Roadmap creation & prioritization 

· Business problem decomposition 

3. Solution Architecture Awareness

· Ability to design end-to-end AI systems 

· APIs, data pipelines, integrations 

· Cloud-native patterns 

4. Execution Leadership

· Drive delivery across teams 

· Stakeholder management 

· Decision-making under ambiguity 

5. Technical Fluency

· Comfortable with: 

o Python / SQL (at least working level) 

o Data platforms (Snowflake, Databricks, etc.) 

o Cloud (Azure / AWS / Google Cloud Platform) 

Secondary Skills (Good-to-Have):

These made candidates stronger but weren’t strict requirements:

1. Hands-on GenAI Tooling

· LangChain / Semantic Kernel / vector DBs 

· Fine-tuning / embeddings familiarity 

2. Agile & Delivery Frameworks

· Scrum / SAFe 

· Product operating models 

3. Data & Analytics Depth

· Experimentation (A/B testing) 

· Metrics design for AI products 

4. UX for AI

· Conversational design 

· Human-in-the-loop systems 

5. Domain Experience

· Industry-specific AI use cases (finance, healthcare, etc.)