Solution Architect - AI & AI Governance

Job Description: Solution Architect - AI & AI Governance

Company: SCIKIQ Data Private Limited

Type - Product & Services

Location: Gurugram, Sector 32

Work Mode: Work from Office (5 days a week)

Experience: 10+ Years (3+ in AI / GenAI)

Employment Type: Full-time

About SCIKIQ

SCIKIQ is an AI-native, no-code data platform from Gurugram. Enterprises use it to connect their data ,govern it (catalog, lineage, data quality, policies) and use it through dashboards, data products and AI copilots driving relevant use cases.

The Role

You design the AI solutions our customers run on SCIKIQ - conversational analytics, copilots, GenAI apps and AI agents - on top of governed data. You also set up the AI governance that keeps them accurate, safe and auditable.

What You Will Do

• AI solutions: Design and deliver natural-language query, copilots, GenAI apps and AI agents on SCIKIQ for enterprise customers.
• AI context layer: Connect glossary, semantic models, lineage and access rules to prompts, RAG and agents so answers are correct and permission-aware.
• AI governance: Set up use-case risk classification, guardrails (PII out of prompts, output checks), human-in-the-loop, evaluation / hallucination testing, monitoring and audit trails.
• Data governance for AI: Use SCIKIQ catalog, lineage, quality and trust scores to decide which data is safe for AI and show the evidence.
• Delivery: Run workshops, pick the right LLMs and patterns, build POCs, write solution designs and take solutions to production.
• Pre-sales: Demo AI capabilities and explain AI governance to CDOs, CISOs and compliance teams in plain language.
• Work with the DWH architect: Agree on data contracts, definitions and access rules with the DWH Solution Architect. Solve use cases together.
  
  

Must have

• 10 - 15 years in data warehousing and ML architecture experience
• 3+ years delivering GenAI solutions for enterprises.
• Hands-on with LLM apps: Prompts, RAG, embeddings / vector search, tool calling, agent frameworks, LLM evaluation.
• Working knowledge of ML basics and MLOps.
• Strong Python
• Good SQL required and should be comfortable with warehouses / lakehouses.
• Hands-on AI governance: Risk assessment, guardrails, monitoring, explainability, audit evidence
• Understands Data governance (catalog, lineage, quality, access) and AI security (data leakage, prompt injection).
• Explains AI and its risks simply to business, risk and compliance teams. Comfortable on the ground with customers. Should have very good communication skill