Senior AI Engineer - Data Science & Business Process Leadership

Job Description

Sr.Data Scientist Connected Assets 600,000 across 40 industries globally Digital Products Powered 13 (WQ IQ, CIP IQ, Aqua IQ, Dish IQ, Pest Intelligence, Kitchen IQ, HX IQ, and more) Digital Revenue Enabled $370M annually Data Points Processed 250 billion per year from industrial IoT controllers Team Size 150 domain experts across 6 global locations Global Locations Pune (India), Naperville (USA), Jeddah (Saudi Arabia), and 3 additional sites Alarm Automation Rate 88% and growing through AI-driven transformation TVD Generated $22M in 2025, on track for $100M in 2026 EGIC 2.0 Transformation Context EGIC is undergoing a fundamental transformation from reactive operations (EGIC 1.0) to an autonomous intelligence engine (EGIC 2.0) through two strategic programs: AIM (AI Integration & Modernization): Embedding AI across all EGIC workflows automating reactive alarm triage, building predictive intelligence, and liberating 2530 FTE-equivalent capacity through agentic automation. APEX (Adoption, Proactive Engagement & eXpansion): Driving digital adoption, converting hardware-only customers to digital subscribers, and scaling TVD from $22M to $100M through proactive customer engagement. This role is central to both programs the candidate will lead the AI team that powers AIM and provides the intelligence backbone for APEX. 3. Role Summary 3.1 The Opportunity We are looking for a Sr AI Engineer a rare hybrid professional who combines deep business process understanding with advanced AI/Data Science technical expertise and proven people leadership. This individual will lead a team of 56 data scientists and AI engineers, driving the daily AI operations, project delivery, model deployment, adoption enablement, and production triage across EGICs entire digital product portfolio. The ideal candidate has walked a deliberate career path: starting with understanding how business processes work (BPA), then building the AI that transforms those processes (Data Science), and now leading the team that operates, scales, and governs that AI at enterprise grade (Leadership). This combination ensures the candidate doesnt just build technically impressive models, but builds the RIGHT models that solve REAL operational problems and deliver MEASURABLE business outcomes. 3.2 What Makes This Role Unique You will lead AI operations for one of the largest industrial IoT intelligence centers in the world 600K connected assets, 250B data points/year Your models will directly impact water conservation, energy efficiency, and sustainability outcomes for customers across 40 industries globally You will have end-to-end ownership: from identifying the business problem (BPA lens) designing the AI solution (DS lens) deploying and scaling it (Ops lens) measuring its impact (Leadership lens) You will build and shape a team from a position of influence defining the culture, technical standards, and the operational playbook Direct visibility to EGIC Director and senior leadership your work directly drives strategic decisions and commercial outcomes ($100M TVD target) Cutting-edge technology: Databricks, Azure, LLMs, agentic AI, RAG pipelines, edge computing, real-time IoT streaming 3.3 This Role Is NOT NOT a pure research/academic role we need production engineers, not just experimenters NOT a people-only management role you must be technically hands-on and review architecture decisions NOT a single-project role you will manage a portfolio of 35 concurrent AI projects across different digital products NOT isolated from the business you will regularly interact with Operations, Product, Engineering, Field, and Customer-facing teams 4. Key Responsibilities 4A. Business Process Analysis & Operations Leadership Your BPA foundation differentiates this role from a typical Data Science lead. You will use process analysis expertise to ensure every AI solution is grounded in a deep understanding of the operational workflow it serves. AI without process understanding leads to technically impressive but operationally useless solutions. i. End-to-End Process Mapping & Analysis Map all EGIC operational workflows across 13 digital products from data ingestion alarm generation triage insight creation customer delivery value capture (TVD) Identify human touchpoints, decision nodes, escalation paths, and handoff points in each workflow Quantify time spent, error rates, and throughput at each process step to identify automation ROI Use BPMN (Business Process Modeling Notation) or equivalent tools to create standardized, version-controlled process documentation ii. Automation Opportunity Identification Systematically identify and prioritize automation candidates using a structured scoring framework (Impact Feasibility Urgency) Distinguish between rule-based automation (RPA), ML-based automation (predictive models), and agentic automation (LLM-powered agents) recommending the right approach for each use case Build

Keyskills :
Data ScienceBusiness Process AnalysisLeadershipMachine LearningIoTData AnalysisProcess AutomationTechnical Leadership