Smart Manufacturing- Pharma AI

Job Description

Presales & opportunity shaping Lead discovery workshops to shape problem statements, value cases, target architecture, and transformation roadmaps for pharma manufacturing. Proposal / RFP Leadership own proposal response strategy, solutioning, estimation, delivery approach, risk assumptions, pricing inputs, and develop reusable POVs/demos. Excellent storyboarding, value articulation, case writing, assumptions/risks/dependencies, and capability of creating executive-ready decks is mandatory. Estimation & planning lead delivery estimation and planning across work breakdown, resourcing, timelines, delivery model, and quality gates. Commercial acumen: Good understanding of SOW constructs, milestones, governance, and input to pricing/commercials (with appropriate internal support). Client Advisory acts as trusted advisor by bringing strong consulting capabilities, and proven delivery methodologies to influence decision-making through executive communication and structured recommendations.Program Leadership: Own multi-workstream delivery governance across sites; manage stakeholders, vendors and distributed teams. MES / eBR Leadership drive paperless manufacturing—requirements and solution design across eBR/eMBR, recipe/batch execution, materials & genealogy, weigh & dispense, and exceptions handling.LIMS / QC Digitization: Lead lab transformation—sample lifecycle, test workflows, stability, reporting/COA—and standardize QC processes and data models. Digital Quality (eQMS/QMS) lead deviations, CAPA, change control, audits, training, document control and supplier quality digitization with audit-ready workflows.IT/OT & Data Architecture: Define integration blueprints across MES, ERP, LIMS, QMS, Historian, and SCADA, with contextualized data architecture for end-to-end traceability.AI Enablement: Identify and lead AI/advanced analytics use cases (deviation insights, drift detection/predictive quality signals, process optimization, reliability analytics) with governance, traceability and controlled rollout. Strong experience across at least two of MES/eBR, LIMS, or eQMS/QMS, with integration/data platform exposure; Pharma MES experience (Werum PAS-X, PharmaSuite, Opcenter Pharma) is a plus.Experience of managing discussions with clients, CXO level engagements, advisory roles. Exposure to ISA-95/ISA-88, MESA standards, manufacturing data contextualization, IIoT/historian integrations.Experience with cloud data platforms, MLOps concepts, and model monitoring/governance in regulated settings. Agile delivery (Scrum/SAFe), Lean/Six Sigma; alliance/vendor ecosystem experience; building demos/POVs.AI enablement experience: delivered/led 1–2 analytics/AI initiatives (PoC/MVP/scale) in manufacturing/quality contexts.

Required Skills