AI Project Manager

Techmagnate - A Digital Marketing Agency; New Delhi; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Techmagnate - A Digital Marketing Agency in India seeks an AI-PM to lead the strategic AI transformation across finance, HR, operations, sales, marketing and customer support, bridging business leadership with technical teams and external vendors.

You will design a phased AI roadmap, oversee end-to-end automation workflows, evaluate build vs buy, model financial impact, and deliver monthly KPI dashboards while championing data governance and an AI-first culture.

Qualifications

• Education: Bachelor's or Master's degree in CS, IT, Business Admin or analytical field.
• Professional Background: 5–9 years in project management with AI deployment or enterprise transformation.
• Delivery Execution: End-to-end portfolio tracking using Agile/Scrum/hybrid methods.
• Integration Literacy: Hands-on with cloud workflow engines (n8n, Make, Zapier, Power Automate).
• Commercial Competence: Track ROI and cost-mitigation for tech investments.

Responsibilities

• High-Impact Strategy: Define and execute phased AI roadmap across functions.
• Prioritization: Assess initiatives based on feasibility, ROI, and impact.
• Market Awareness: Produce quarterly State of AI landscape reports.
• Workflow Automation & Platform Architecture: Design end-to-end automation workflows; governance; build vs buy analyses.
• Vibe Coding, Prototyping & Technical Translation: Lead rapid prototyping; translate requirements for stakeholders.
• Engineering Oversight: Coordinate with engineering; ensure velocity and safety.

Skills

Executive Communication

Workflow Platforms

Change Management

Vibe Coding

Strategic Thinking

BR Analysis

Database & API

Decision Making

Data Governance

Cross-Functional Collaboration

AI-First Mindset

Profit Impact

Innovation

Data Metrics

Education

Bachelor's or Master's degree

Tools

n8n

Make

Zapier

Power Automate

Job description

Enterprise Technology & Digital Transformation Lifecycle

Strategic Objective

The AI-PM is a strategic, execution-focused leader driving the company's AI transformation journey. This individual bridges the gap between business leadership, technical teams, and external vendors to deploy company-wide automation, save time, reduce costs, and dramatically improve organizational output quality.

Key Success Metrics

• Processing & Time Savings: Measurable reduction in manual processing time per department and total corporate hours saved.
• Financial Impact: Positive, systematically tracked ROI achieved within defined product and project timelines.
• Adoption & Velocity: High active adoption rates of automated tools across business units and consistent quarterly AI deployments.
• Quality & Satisfaction: Improved pre- vs. post-implementation quality parameters and verified employee satisfaction indexes.

2. Core Responsibilities

• High-Impact Strategy: Define and execute a phased, high-impact AI roadmap aligned with overarching business objectives across Finance, HR, Operations, Sales, Marketing, and Customer Support.
• Prioritization: Assess and prioritize technical initiatives based on operational feasibility, pipeline impact, and financial ROI potential.
• Market Awareness: Monitor global technology shifts, benchmark competitor ecosystems, and deliver a quarterly 'State of AI' landscape report to the executive leadership team.

Workflow Automation & Platform Architecture

• System Integration: Design and oversee end-to-end automation workflows that integrate smoothly with existing enterprise applications (CRM, HRMS, Legacy Databases, etc.).
• Platform Mastery: Demonstrate strong hands-on mastery of modern enterprise workflow tools such as n8n, Make, Zapier, or Microsoft Power Automate.
• Governance & Compliance: Establish robust AI governance, data privacy parameters, continuous model-monitoring controls, and ethical compliance frameworks from day one.
• Build vs. Buy Analysis: Evaluate complex commercial build vs. buy solutions with structured technical and financial risk analysis.

Vibe Coding, Prototyping & Technical Translation

• Rapid Prototyping: Utilize 'vibe coding' principles using natural language inside AI development shells (Cursor, Replit AI, GitHub Copilot, v0) to accelerate prototype deployment without engineering bottlenecks.
• Discovery Engineering: Lead discovery workshops with business heads to translate practical pain points into crisp system requirements, user stories, and precise technical briefs.
• Quality Assurance Check: Evaluate early low-code/vibe-coded prototypes for production readiness before passing them to core developer lines.
• Financial Modeling: Build data-grounded financial models projecting cost optimizations, capacity release, and structural quality returns to capture leadership approval.
• KPI Dashboards: Monitor and distribute detailed monthly dashboards reporting target vs. actual ROI performance benchmarks.
• Vendor Optimization: Serve as the master point of contact for external AI software companies, SaaS vendors, and specialized system integrators; negotiate precise SLAs.
• Executive Advisory: Translate complex, multi-layered algorithmic and machine learning engineering terms into simplified, accessible strategic vocabulary for non-technical stakeholders.
• Culture Catalyst: Champion a data-driven, AI-first work culture through internal continuous learning series, lunch-and-learn presentations, and proactive change management strategies.

Engineering Oversight & Architecture (Added Advantage)

• Velocity Protection: Partner directly with engineering lines, data scientists, and DevOps specialists to break execution blockers and maintain delivery timeline momentum.
• Cross-Functional Operations: Directly coordinate, mentor, and track performance across a cross-functional Agile unit comprising developers, data analysts, system architects, UI/UX engineers, and QA assets.
• Psychological Safety: Construct a highly collaborative, innovative, and safe team environment that incentivizes experimentation, speed, and learning from failure.
• Resource Optimization: Control tactical resource allocation patterns to avoid personnel burnout while defending roadmap velocity commitments.

3. Qualifications & Competencies

Required Qualifications

• Education: Bachelor's or Master's university degree in Computer Science, Information Technology, Business Administration, or a heavily analytical engineering field.
• Professional Background: 5–9 years of structural project management experience, with a validated minimum of 3 years strictly dedicated to AI deployment, complex workflow automation, or enterprise-scale digital transformations.
• Delivery Execution: Proven, end-to-end portfolio tracking product evolution from conceptual design phase through to live enterprise rollout using Agile, Scrum, or hybrid workflow methods.
• Integration Literacy: Verifiable hands-on track record using cloud workflow orchestration engines (n8n, Make, Zapier, or Power Automate).
• Commercial Competence: Documented background creating financial case summaries, tracking cost-mitigation parameters, and proving tech investments ROI.

Core Competency Matrix

• Executive-Level Communication
• Workflow Automation Platforms
• Change Management & Empathy
• Vibe Coding & Low-Code Deployment
• Strategic Thinking
• Business Requirements Analysis
• Database & API Knowledge
• Decision-Making Under Uncertainty
• Data Governance & AI Ethics
• Cross-Functional Collaboration
• AI-First Mindset: We operate in an aggressive, progressive corporate landscape where internal workflow automation and AI scaling are resourced as primary, non-negotiable competitive business advantages.
• High Market Impact: This position holds deep strategic authority with real-time, measurable influence over company-wide profit sheets, employee operational capacity, and structural framework maturity.
• Innovation Incubator: Personnel are consistently encouraged, measured, and expected to proactively test, pilot, and pitch novel AI-augmented infrastructure solutions directly to executive directors.
• Rigorous Data Foundations: All operational paths and tool deployment architectures are cleanly metrics-driven. You will operate with open access to precise data analytics layers and performance dashboards to direct strategic priorities.