SENIOR MANAGER, INFORMATION TECHNOLOGY

1. AI/GenAI Platform Delivery & Technical Program Management

Lead the technical planning and delivery of enterprise AI, Gen AI, Data Lake, Analytics, and Cloud platform initiatives. Define technical workstreams, architecture dependencies, platform onboarding strategies, implementation roadmaps, and delivery milestones to ensure scalable and production-ready solutions.

2. Technical Requirements Engineering & Solution Analysis

Analyse business requirements, source systems, data models, database schemas, API specifications, payload structures, and integration requirements. Prepare functional specifications, user stories, acceptance criteria, process flows, and technical documentation for engineering teams. Perform SQL-based analysis to validate business rules, data mappings, and transformation requirements. Analyse business requirements, source systems, data models, database schemas, API specifications, payload structures, and integration requirements. Prepare functional specifications, user stories, acceptance criteria, process flows, and technical documentation for engineering teams. Perform SQL- based analysis to validate business rules, data mappings, transformation requirements, and data quality metrics.

3. AI/Gen AI Solution Design & Technical Evaluation

Participate in technical design discussions for AI and GenAI solutions including RAG architecture, knowledge ingestion pipelines, vector databases, document chunking strategies, embedding models, LLM integrations, and prompt engineering approaches. Evaluate technical feasibility, data readiness, scalability, and deployment considerations for enterprise AI use cases. Review enterprise knowledge repositories, data ingestion strategies, retrieval approaches, and model evaluation criteria to ensure AI solutions meet accuracy, scalability, and operational requirements.

4. Enterprise Data Platform & Cloud Engineering Coordination

Coordinate implementation of enterprise data platforms, AWS services, data lakes, ETL/ELT pipelines, analytics environments, and system integrations. Review data ingestion processes, transformation logic, platform configurations, and cloud architecture designs to ensure performance, reliability, and scalability requirements are met.

5. Technical Architecture & Engineering Governance

Collaborate with Solution Architects and Engineering Leads to review architecture designs, API contracts, integration specifications, microservices interactions, observability frameworks, monitoring solutions, logging standards, and security requirements. Assess technical trade-offs and implementation approaches.

6. Agile Technical Delivery Management

Manage backlog refinement, sprint planning, release planning, estimation reviews, dependency tracking, resource planning, and execution monitoring across AI, Data Engineering, Analytics, QA, Architecture, and DevOps workstreams. Ensure technical deliverables are aligned with business and platform objectives.

7. Technical Risk, Incident & Dependency Management

Analyse production incidents, application logs, monitoring dashboards, platform alerts, integration failures, data quality issues, and API errors. Coordinate root-cause analysis with Engineering and DevOps teams and drive remediation plans to improve platform stability and operational resilience.

8. Technical Documentation & Solution Governance

Maintain technical specifications, architecture documents, API definitions, integration documents, solution designs, data flow diagrams, governance artefacts, implementation plans, status reports, and engineering decision logs. Establish technical standards and reusable delivery templates.

9. AI-Assisted Engineering & Productivity Enablement

Leverage AI tools such as ChatGPT and Claude to support requirements analysis, user story creation, acceptance criteria definition, technical documentation, workshop summarization, solution evaluation, and engineering collaboration, improving delivery efficiency and consistency.

Required Skills & Competencies
1. Master's Degree (Information Technology Management, or equivalent).

2. Strong experience managing large digital transformation programs.
3. Expertise in Agile, Scrum, Waterfall, and Hybrid delivery models.
4. Experience managing multi-vendor and distributed delivery teams.
5. Strong project planning, budgeting, risk management, and governance capabilities.

6. Strong understanding of: Artificial Intelligence(AI), Machine Learning (ML),Data Engineering, Analytics Platforms, ML Ops, Cloud Infrastructure, AI Deployment Architectures
7. Familiarity with enterprise-scale AI adoption and automation programs
8. Excellent customer-facing and executive communication skills.
9. Ability to influence stakeholders across technical and business functions.
10. Strong problem-solving and decision-making capabilities.
11. Experience presenting program status and business value realization to Project Leadership
12.Exposure to AI-driven network operations, AIOps, or Autonomous Network programs.