AI Account Delivery Lead

Job description

Job Description Job Role: AI Delivery Lead Who we are: - At CitiusTech , we constantly strive to solve the industry's greatest challenges with technology, creativity, and agility. With over 8,500 healthcare technology professionals worldwide, CitiusTech powers healthcare digital innovation, business transformation, and industry-wide convergence for over 140 organizations through next-generation technologies, solutions, and products. We aim to accelerate the transition to a human-first, sustainable, and digital healthcare ecosystem with the world's leading Healthcare and life sciences organizations and our partners. Here is an opportunity for you to make a difference and collaborate with global leaders to shape the future of healthcare and positively impact human lives. What is in it for you? The AI Delivery Lead is a senior leadership role responsible for end-to-end delivery of AI and GenAI projects for enterprise customers. This individual will serve as the primary delivery owner across the full project lifecycle — from discovery and solution design through implementation and go-live — ensuring that projects are delivered on time, within budget, and aligned with customer outcomes. The role combines strategic project leadership with deep technical fluency in AI/ML, enabling the AI Delivery Lead to bridge the gap between business stakeholders, technical teams, and customer leadership. This person will be hands-on during discovery and planning phases and shift to a governance and review role during implementation, while maintaining overall accountability for delivery quality and resource utilization. Key Responsibilities 1. Discovery & Assessment Phase • Lead customer discovery workshops to identify high-value AI use cases, assess data readiness, and define success criteria • Conduct current-state assessment of customer’s technology landscape, data infrastructure, and organizational AI maturity • Collaborate with solution architects and data engineers to evaluate feasibility, estimate effort, and identify technical risks • Develop discovery deliverables including use case prioritization matrices, data readiness scorecards, and preliminary solution hypotheses • Engage customer stakeholders (IT, business, clinical/operations) to align on scope, timelines, and expected business outcomes 2. Planning & Solution Design Phase • Define project scope, work breakdown structure (WBS), milestones, dependencies, and delivery timelines • Partner with AI/ML architects to design solution architecture covering data pipelines, model training, serving infrastructure, and integration touchpoints • Establish project governance framework including steering committees, escalation paths, RACI matrices, and communication cadence • Create staffing plans and resource allocation strategies across onshore, offshore, and customer teams • Define acceptance criteria, quality gates, and definition of done for each sprint and phase • Identify and document project risks with mitigation and contingency strategies 3. Implementation Phase (Governance & Review) • Serve as the delivery governance lead, conducting sprint reviews, code and architecture reviews, and quality checkpoints • Monitor delivery velocity, burndown metrics, and team health; intervene proactively when delivery is at risk • Review technical artifacts including model evaluation reports, data pipeline designs, API specifications, and deployment runbooks • Ensure adherence to coding standards, MLOps best practices, security protocols, and compliance requirements • Facilitate customer demos, UAT cycles, and sign-off processes at each milestone • Act as the escalation point for technical blockers, scope changes, and cross-team dependencies 4. Resource Management & Delivery Operations • Own day-to-day delivery operations ensuring sprint commitments are met and impediments are cleared within 24 hours • Manage project team composition including onboarding, skill-gap assessment, and performance feedback for delivery resources • Optimize resource utilization across concurrent AI engagements, balancing workload and bench time • Coordinate with the talent and hiring team to forecast resourcing needs and participate in technical interviews • Manage project financials including budget tracking, effort variance analysis, and margin reporting 5. Customer Relationship & Stakeholder Management • Serve as the single point of accountability for the customer on all delivery matters • Conduct weekly and monthly status reviews with customer leadership, presenting progress, risks, and decisions needed • Build trusted advisor relationships with customer CIOs, CDOs, VPs, and line-of-business owners • Identify upsell and expansion opportunities during engagements and partner with sales/account teams to pursue them Required Qualifications • 12–15 years of total IT experience with at least 4–5 years in delivery management of AI/ML or data engineering projects • Demonstrated experience leading teams of 10–25+ across onshore/offshore delivery models • Strong understanding of ML lifecycle including data preparation, feature engineering, model training, evaluation, deployment, and monitoring • Experience with GenAI technologies including LLMs, RAG architectures, prompt engineering, vector databases, and agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI) • Proficiency in at least one cloud platform (AWS, GCP, or Azure) with understanding of AI/ML managed services • Experience with Agile/Scrum delivery at scale; certified Scrum Master or SAFe credentials preferred • Excellent verbal and written communication skills with the ability to present to C-suite and technical audiences • Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field Preferred Qualifications • Prior experience delivering AI solutions in healthcare (payer, provider, pharma, or life sciences verticals) • Fam