Sr AI Solutions Architect

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 : AI / GenAI Expertise LLMs, RAG, copilots, NLP Prompt engineering basics Understanding of ML lifecycle Product + Strategy Thinking Strong product management mindset Roadmap creation & prioritization Business problem decomposition Solution Architecture Awareness Ability to design end-to-end AI systems APIs, data pipelines, integrations Cloud-native patterns Execution Leadership Drive delivery across teams Stakeholder management Decision-making under ambiguity Technical Fluency Comfortable with: Python / SQL (at least working level) Data platforms (Snowflake, Databricks, etc.) Cloud (Azure / AWS / Google Cloud Platform) Secondary Skills (Good-to-Have) These made candidates stronger but weren t strict requirements: Hands-on GenAI Tooling LangChain / Semantic Kernel / vector DBs Fine-tuning / embeddings familiarity Agile & Delivery Frameworks Scrum / SAFe Product operating models Data & Analytics Depth Experimentation (A/B testing) Metrics design for AI products UX for AI Conversational design Human-in-the-loop systems Domain Experience Industry-specific AI use cases (finance, healthcare, etc.)