AI Platform Engineer (Governance Architecture & Platform Engineering)

ValueLabs; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

ValueLabs in Hyderabad, India, seeks a Senior Platform & AI Governance Architect to lead the design and implementation of a secure, scalable AI governance platform across multi-tenant environments. This role shapes how teams build, deploy, and monitor AI agents from ideation to production.

You will own a multi-tier governance framework, integrate with Purview, Azure AI Foundry, Copilot Studio, Unity Catalog, and MLflow, and establish auditability, risk scoring, and deployment gates to meet

Qualifications

• 6+ years in platform engineering, solutions architecture, AI/ML engineering, or integration architecture.
• Deep experience with AI/LLM fundamentals: prompt versioning, tool calls, evaluation, and agent tracing.
• Proven track record in integration architecture: APIs, webhooks, iPaaS, SaaS connectors.
• Strong cloud expertise across Azure, Kubernetes, networking, observability, and CI/CD.

Responsibilities

• Architect and operationalize an AI governance platform across multi-tenant environments.
• Design multi-tier governance layer with registries, catalogs, and real-time guardrails.
• Develop risk scoring and escalation for cross-domain governance and auditability.
• Deliver executive dashboards for spend visibility and governance posture.

Skills

Platform engineering

Solutions architecture

AI/ML engineering

Integration architecture

AI governance

Cloud for AI

Tools

Purview

Azure AI Foundry

Copilot Studio

Unity Catalog

MLflow

Job description

We're seeking a visionary Senior Platform & AI Governance Architect to lead the design and implementation of a secure, scalable, and compliant Agentic AI platform — one that powers intelligent workflows across complex, multi-tenant environments.

If you're passionate about shaping the future of AI at scale — where security, governance, and performance go hand-in-hand — this is your moment.

What You'll Own

You’ll architect and operationalize a next-generation AI Governance & Platform Engineering framework that enables safe, auditable, and enterprise-ready AI deployment. Your work will directly impact how teams across the organization build, deploy, and monitor AI agents — from ideation to production.

• Design and implement a multi-tier AI governance layer, including agent registries, tool/connector catalogs, identity-scoped permissions, and real-time guardrails across mission-critical workflows.
• Build and maintain an AI Use-Case Registry & Inventory with lifecycle tracking, vendor AI visibility, and unique use-case IDs for audit and compliance.
• Develop a Risk Tier Classification Engine to automate risk scoring, escalation triggers, and support for multi-tenancy and cross-domain risk modeling.
• Architect Logging & Traceability infrastructure capturing prompts, model versions, outputs, overrides, and timestamps — ensuring full auditability across use cases.
• Implement Kill-Switch & Rollback Controls with documented disable paths, owner assignments, and audit trails for rapid incident response.
• Champion native integrations with Microsoft and Databricks ecosystems — including Purview, Azure AI Foundry, Copilot Studio, Unity Catalog, and MLflow — to reduce technical debt and improve maintainability.
• Drive multi-entity governance, ensuring data isolation, entity-level stewardship, and cross-entity auditability without data commingling.
• Define human oversight & escalation workflows, including routing logic, named owners, and cross-entity decision paths.
• Own deployment governance with 5-gate freeze rules, ADR integration, approval workflows, and exception management.
• Ensure compliance with FERPA, GLBA, SOC 2 Type II, and other regulated frameworks through policy-as-code and automated attestation.
• Deliver executive dashboards for spend visibility, risk posture, review cadence, and AI Control Authority reporting.
• Translate governance strategy into actionable technical designs, collaborating with Infrastructure, InfoSec, Compliance, and Engineering teams.

Required Qualifications

• 6+ years of hands‑on experience in platform engineering, solutions architecture, AI/ML engineering, or integration architecture — ideally in regulated or enterprise environments.
• Deep practical experience with AI/LLM engineering fundamentals: prompt versioning, tool calling, RAG, evaluation design, hallucination detection, and agent tracing.
• Proven track record in integration architecture — APIs, webhooks, iPaaS, MCP servers, and SaaS connectors.
• Strong cloud expertise across Azure, including Kubernetes, networking, secrets management, observability, and CI/CD pipelines.
• Solid grasp of security fundamentals: RBAC, SSO, OAuth, service principals, PII handling, data masking, and audit logging.
• Proficiency in SQL and data analytics — dashboards, cost attribution, trace analysis, event modeling, and warehouse/lakehouse concepts.
• Experience with governance & compliance frameworks — SOC 2, GDPR, retention policies, approval workflows, and evidence collection.
• Demonstrated product mindset — experience building tenant‑level reporting, customer‑facing controls, enterprise admin UX, or release gates.
• Strong understanding of enterprise systems architecture, interoperability, and the ability to evaluate and select AI governance tooling.

Preferred Qualifications

• Experience designing or implementing AI governance frameworks (e.g., agent registries, risk tiering engines, deployment gate workflows) in production.
• Hands‑on experience with Microsoft & Databricks‑native AI/governance tools: Purview, Azure AI Foundry, Copilot Studio, Unity Catalog, MLflow.
• Familiarity with regulated‑sector compliance (e.g., FERPA, GLBA).
• Built kill‑switch, rollback, or incident‑response mechanisms for AI or SaaS systems.
• Exceptional communication and documentation skills — able to articulate risk tradeoffs and governance recommendations to both technical and executive audiences.