AI agentic engineer

Valuelabs; Hyderabad; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Valuelabs is seeking an experienced AI Governance Architect to lead governance architecture and platform engineering for AI-enabled services across student journey workflows. You will design a comprehensive governance layer, agent registry, tool/connector catalog, and real-time guardrails to ensure compliant and auditable AI outputs.

The role requires 5–10 years in IT services/consulting with expertise in AI and cloud deployments, including risk scoring, policy-driven controls, and cross-entity

Qualifications

• 5-10 years of experience in IT services & consulting with expertise in AI engineering.
• Strong understanding of machine learning concepts, including supervised/unsupervised methods and deep learning techniques.
• Experience working on large datasets to extract insights that drive business value.
• Familiarity with cloud-based platforms (e.g., Azure) for deploying AI models.

Responsibilities

• Governance Architecture & Platform Engineering for AI systems and student journey workflows.
• Architect and implement an Agentic AI Governance layer with registry, tool catalog, and real-time guardrails.
• Create an AI Use-Case Registry & Inventory with lifecycle tracking and entity-level visibility.
• Build a Risk Tier Classification Engine supporting multi-tenant risk scoring and escalation.

Skills

AI engineering

Machine learning concepts

Large datasets

Azure cloud

Job description

Roles and Responsibilities

• Governance Architecture & Platform Engineering

• Architect and implement an Agentic AI Governance layer, including an agent registry, tool/connector catalog, identity-scoped permissions, and real-time guardrails on LLM and tool-call outputs across student journey workflows.
• Design and maintain an AI Use-Case Registry & Inventory, providing central intake, vendor AI inventory tracking across CRM/LMS/SIS, unique use-case IDs, lifecycle state tracking, and entity-level visibility.
• Build a Risk Tier Classification Engine supporting automated and policy-driven multi-tier assignment, risk scoring, escalation triggers, and support for multi-tenancy and cross-domain risk axes.
• Design and maintain Logging & Traceability infrastructure capturing prompts/hashes, model version, output type, override flags, and timestamps, ensuring a full audit trail for the multi-tier use cases.
• Architect Kill-Switch & Rollback Controls, including documented per-use-case and per-vendor-feature disable paths, rollback documentation, kill-switch owner assignment, and auditability of disable events.
• Evaluate and recommend the most appropriate AI governance, evaluation, and observability tools and frameworks, balancing scalability, integration, and maintenance considerations.

• Cross-Functional Collaboration & Multi-Entity Governance

• Under the direction of the Director, design and implement Multi-Tenant / Multi-Entity Support, including entity-level data isolation, per-entity steward configuration, and cross-entity audit without data commingling.
• Define and implement Human Oversight & Escalation Routing, including named oversight owner assignment, escalation workflows, and cross-entity routing logic determining which entity steward is triggered for which agent event.
• Partner with counterparts across Infrastructure, InfoSec, Compliance, and Applications Engineering to align on security, governance, deployment, and performance standards.
• Support the translation of high-level governance and compliance objectives into detailed technical designs, guiding implementation across teams.

• Delivery, Compliance & Operationalization

• Own Deployment Governance & Gate Workflow processes, enforcing the 5-gate freeze rule, integrating with ADRs, and supporting approval routing, AI Control Authority sign-off, and exception management.
• Ensure Compliance & Regulatory Alignment with FERPA, GLBA, SOC 2 Type II, and higher-ed sector frameworks, implementing policy-as-code for regulatory requirements and supporting automated compliance attestation.
• Deliver Microsoft + Databricks Native Integration wherever possible – including Purview, Azure AI Foundry, Copilot Studio, Unity Catalog, and MLflow – favoring native connectors over custom middleware.

Job Requirements

• 5-10 years of experience in IT services & consulting with expertise in AI engineering.
• Strong understanding of machine learning concepts, including supervised/unsupervised methods, deep learning techniques, etc.
• Experience working on large datasets to extract insights that drive business value.
• Familiarity with cloud-based platforms (e.g., Azure) for deploying AI models.