Sr AI Solutions Architect

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)

• 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:

• 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:

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.)