AI Tech Lead

Trigent Software Private Limited

Job Location:

Bengaluru - India

Monthly Salary: Not Disclosed

Posted on: 19 days ago

Vacancies: 1 Vacancy

Job Summary

Virtusa is a global product and platform engineering services company that makes experiences better with technology. We help organizations grow faster more profitably and more sustainably by reimagining enterprises through domain-driven solutions. We combine strategy design and engineering backed by unmatched expertise at the intersection of industry business and technology to generate real-world business impact for clients. Headquartered in Massachusetts with global delivery centers Virtusa provides a broad range of services solutions and assets including strategy and design AI advisory and services digital engineering data and analytics digital assurance cloud and security cx transformation and managed services across industries such as financial services healthcare communications media entertainment travel manufacturing and technology.

Mandatory Skills

generative aiPythonAWSEnterprise ArchitectureAgentic AiModel Context Protocol (MCP)

Skill to Evaluate

generative aiPythonAWSEnterprise ArchitectureAgentic AiModel Context Protocol (MCP)

Enterprise Experience: 7 years of experience managing technical IT projects with a strong emphasis on IT Service Management (ITSM) Application Management Services (AMS) or enterprise software delivery.

Generative AI Competency: Proven architectural understanding of Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) and system token optimization.
Integration Expertise: Hands-on experience or profound structural knowledge of connecting conversational layers to enterprise backend systems (specifically SAP S/4HANA via APIs JIRA and BMC Helix environments).
Protocol & Security Familiarity: Conceptual grasp of modern AI connectivity paradigms like Model Context Protocol (MCP) vector databases and enterprise data masking methodologies.
Framework Orientation: Familiarity with global AI governance standards (e.g. NIST AI RMF ISO/IEC 42001) is highly desirable .

Job Title

AI Tech Lead

Roles & Responsibilities

Key Responsibilities

1. Solution Adoption & Implementation Ownership

Drive AI Adoption: Lead the end-to-end adoption roadmap of the enterprise AI solution across multiple projects platforms and geographic regions.
Proof of Concept to Production Lifecycle: Own the execution of complex Generative AI use cases from initial validation to full production deployment specifically optimizing automated Level 1 (L1) support workflows and Level 0 (L0) automated system remediation infrastructure.
Value Optimization & Metrics: Set and actively pursue project-level performance optimization KPIs focusing on reducing Mean Time to Resolution (MTTR) maximizing ticket deflection rates and containing operational support costs .
Use Case Ideation: Continuously identify evaluate and prioritize high-impact AI opportunities to systematically reduce IT helpdesk workloads and bottlenecks .

2. Governance Refinement & Analytics

Feasibility & Model Fine-Tuning: Conduct deep technical use-case reviews map data operations and oversee prompt tuning or refinement strategies using our local Large Language Model (LLM) token-optimization infrastructure.
AI Governance & Compliance: Manage compliance data privacy and mandatory risk controls. Ensure strict adherence to information filtering guidelines to prevent data leaks or cross-context contamination.
Performance Reporting: Build maintain and report on real-time operational health metrics and analytical dashboards detailing the impact token costs and accuracy of active AI instances.

3. Feature Scaling & Enterprise Integration

Scalable Feature Rollouts: Collaborate with technical development teams to build and scale modular features within the core platform ensuring they are seamlessly packaged for rapid project-level implementation.
Enterprise Integration Maintenance: Supervise the deployment of core technical features across SAP and non-SAP projects utilizing our available technical stack:
- Knowledge Base Integrations: Core knowledge discovery loops mapped to Confluence and SharePoint.
- Ticketing Orchestration Platforms: Direct automated triage workflows feeding into JIRA and BMC Helix.
- Core SAP Connectivity: Interfacing with SAP systems using Informatica API pipelines for S/4HANA connectivity.
- Advanced Agentic Architecture: Leveraging the Model Context Protocol (MCP) Agentic Orchestration and Amazon S3 Vector RAG setups.

Information Security Reviews: Partner directly with corporate InfoSec teams to achieve rapid security clearings and technical risk approvals for all running agent architectures.

4. Program Management & Stakeholder Alignment

Program Tracking: Monitor and track cross-functional implementation progress across all engineering and delivery streams to maintain programmatic momentum.
Centralized Transparency: Keep all historical active and pipeline AI automation efforts meticulously updated on the enterprise program management platform.
Blocker Resolution: Identify key programmatic or technical blockers early facilitating high-clarity communication and alignment across overlapping delivery squads.

Mandatory Skills

generative aiPythonAWSEnterprise ArchitectureAgentic AiModel Context Protocol (MCP)

Skill to Evaluate

generative aiPythonAWSEnterprise ArchitectureAgentic AiModel Context Protocol (MCP)

Generative AI Competency: Proven architectural understanding of Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) and system token optimization.
Integration Expertise: Hands-on experience or profound structural knowledge of connecting conversational layers to enterprise backend systems (specifically SAP S/4HANA via APIs JIRA and BMC Helix environments).
Protocol & Security Familiarity: Conceptual grasp of modern AI connectivity paradigms like Model Context Protocol (MCP) vector databases and enterprise data masking methodologies.
Framework Orientation: Familiarity with global AI governance standards (e.g. NIST AI RMF ISO/IEC 42001) is highly desirable .

Job Title

AI Tech Lead

Roles & Responsibilities

Key Responsibilities

1. Solution Adoption & Implementation Ownership

Drive AI Adoption: Lead the end-to-end adoption roadmap of the enterprise AI solution across multiple projects platforms and geographic regions.
Proof of Concept to Production Lifecycle: Own the execution of complex Generative AI use cases from initial validation to full production deployment specifically optimizing automated Level 1 (L1) support workflows and Level 0 (L0) automated system remediation infrastructure.
Value Optimization & Metrics: Set and actively pursue project-level performance optimization KPIs focusing on reducing Mean Time to Resolution (MTTR) maximizing ticket deflection rates and containing operational support costs .
Use Case Ideation: Continuously identify evaluate and prioritize high-impact AI opportunities to systematically reduce IT helpdesk workloads and bottlenecks .

2. Governance Refinement & Analytics

Feasibility & Model Fine-Tuning: Conduct deep technical use-case reviews map data operations and oversee prompt tuning or refinement strategies using our local Large Language Model (LLM) token-optimization infrastructure.
AI Governance & Compliance: Manage compliance data privacy and mandatory risk controls. Ensure strict adherence to information filtering guidelines to prevent data leaks or cross-context contamination.
Performance Reporting: Build maintain and report on real-time operational health metrics and analytical dashboards detailing the impact token costs and accuracy of active AI instances.

3. Feature Scaling & Enterprise Integration

Scalable Feature Rollouts: Collaborate with technical development teams to build and scale modular features within the core platform ensuring they are seamlessly packaged for rapid project-level implementation.
Enterprise Integration Maintenance: Supervise the deployment of core technical features across SAP and non-SAP projects utilizing our available technical stack:
- Knowledge Base Integrations: Core knowledge discovery loops mapped to Confluence and SharePoint.
- Ticketing Orchestration Platforms: Direct automated triage workflows feeding into JIRA and BMC Helix.
- Core SAP Connectivity: Interfacing with SAP systems using Informatica API pipelines for S/4HANA connectivity.
- Advanced Agentic Architecture: Leveraging the Model Context Protocol (MCP) Agentic Orchestration and Amazon S3 Vector RAG setups.

Information Security Reviews: Partner directly with corporate InfoSec teams to achieve rapid security clearings and technical risk approvals for all running agent architectures.

4. Program Management & Stakeholder Alignment

Program Tracking: Monitor and track cross-functional implementation progress across all engineering and delivery streams to maintain programmatic momentum.
Centralized Transparency: Keep all historical active and pipeline AI automation efforts meticulously updated on the enterprise program management platform.
Blocker Resolution: Identify key programmatic or technical blockers early facilitating high-clarity communication and alignment across overlapping delivery squads.