AI Project Manager (Sarita Vihar)

JOB DESCRIPTION: Project Manager – AI Enterprise Technology & Digital Transformation Lifecycle 1. Role Overview Position Title: Project Manager – Artificial Intelligence (AI-PM) Department: Technology / Digital Transformation Experience Required: 5–9 Years (AI / Digital Transformation Projects) Location: On-Site 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 AI Strategy & Roadmap Development
- 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. ROI Analytics & Financial Case Development
- 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, Client & Management Communication
- 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.
- Technical Foundation: Maintain a functional, operational literacy in database structures (MySQL, PostgreSQL, NoSQL architectures) and foundational programming frameworks (Python, JavaScript, REST APIs, JSON data maps) to effectively guide core product decisions. Team Leadership & Personnel Management
- 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.
- Qualified 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. Preferred Capabilities / Added Advantage
- Programming Baseline: Functional literacy or script reading skill in Python, JavaScript, and advanced SQL querying mechanisms.
- ML/LLM Knowledge: Working familiarity with basic machine learning models, fine-tuning structures, vector search, and model APIs (OpenAI, Anthropic, Gemini).
- Prototyping Frameworks: Familiarity using advanced code assistant tools (Cursor, Replit, v0.dev, Copilot).
- Certifications: Active industry credentials (PMP, PMI-ACP, PRINCE2) or specialized vendor certifications from AWS, Microsoft, Google, or DeepLearning.AI. Technical Competencies Leadership Competencies
- AI Strategy & Roadmap Design
- Executive-Level Communication
- Workflow Automation Platforms
- Team Building & Mentorship
- ROI Modeling & Financial Analysis
- Vendor & Stakeholder Management
- AI Platform Architecture
- Change Management & Empathy
- Vibe Coding & Low-Code Deployment
- Strategic Thinking
- Business Requirements Analysis
- Conflict Resolution
- Database & API Knowledge
- Decision-Making Under Uncertainty
- Data Governance & AI Ethics
- Cross-Functional Collaboration 4. Work Environment & Corporate Culture
- 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. Apply on Kit Job: kitjob.in/job/4mo2q6