Raritan, New Jersey, United States of America, United States of America
Hybrid
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Job Function:
Technology Product & Platform ManagementJob Sub Function:
Multi-Family Technology Product & Platform ManagementJob Category:
People LeaderAll Job Posting Locations:
Guaynabo, Puerto Rico, United States of America, Raritan, New Jersey, United States of AmericaJob Description:
We are searching for the best talent for a Director, MedTech Technology AI Orchestration and Enablement to be located in Raritan, NJ or Guaynabo, PR.
The Director will own the vision, roadmap, and delivery of an enterprise AI enablement and orchestration platform that standardizes model deployment, multi‑model/agent orchestration, and production MLOps across cloud and edge. This Director will translate strategic priorities into a prioritized portfolio of capabilities, build reusable platform components and self‑service tooling, enforce governance and security guardrails, and drive measurable business impact through automated, agentic workflows. The role owns a $97M P&L and stewards AI orchestration that enables a $2B business transformation.
Role objective:
Lead the creation and operation of an enterprise AI enablement and orchestration platform that standardizes model deployment and multi‑model/agent workflows, embeds governance and security, and accelerates scalable, cost‑effective AI‑driven automation to deliver measurable business value.
Major Duties & Responsibilities:
AI Orchestration & Enablement Strategy
Define and lead the roadmap for AI enablement and orchestration, aligning platform initiatives to business outcomes and ROI.
Design, build, and operate a unified AI orchestration layer that handles model deployment, inference routing, chaining of agentic workflows, lifecycle automation, and low‑latency routing across cloud and edge.
Deliver an AI enablement platform: feature store, model/catalog, policy engines, observability/metadata, developer toolkits, templates, and self‑service flows to accelerate ML delivery.
Establish production‑grade MLOps (training/validation pipelines, CI/CD, canary/blue‑green deploys, rollback) and automate infra provisioning and cost‑aware inference scaling.
Lead architecture and patterns for agentic AI and multi‑model orchestration (prompt management, chain‑of‑thought routing, tool use, human‑in‑the‑loop).
Financial Outcomes
Deliver demonstrable automation and efficiency outcomes, with responsibility for achieving >$50M annual efficiencies beginning 2027.
Steward financial resources: manage a $55M operating P&L and prioritize ~ $500M in AI investments against ROI and delivery milestones.
Operations and Risk Management
Define and enforce model governance, deployment guardrails, approval gates, risk tiers, and auditability; ensure safety, fairness and explainability are embedded in orchestration.
Partner with Security/ISRM to integrate model/data security controls (access, encryption, secure model stores, secrets).
Operationalize monitoring and observability for models and orchestrations: performance, drift, latency, cost per inference, automated alerting and playbooks.
Drive integration standards and APIs to connect orchestration with business systems (WMS/TMS/CRM/ERP), RPA, and external partners.
Own lifecycle management for reusable AI assets (versioning, lineage, deprecation, discoverable catalog).
Manage vendor/partner strategy for pre‑trained models, hosting platforms and orchestration tooling while enforcing procurement and compliance.
Continuously improve via A/B testing, experimentation frameworks, post‑deployment learning loops and prioritized backlog management.
Define SLAs, runbook‑driven incident response, and ensure legal/regulatory/privacy compliance across jurisdictions.
Implement ethical AI practices (bias testing, human oversight for high‑risk actions, transparent decision logs, third‑party audits).
Talent & people management focus areas
Lead change management and enablement: train developers, data scientists, IT ops and business users on patterns, components and operational best practices.
Build and grow a cross‑functional team (ML engineers, MLOps, SRE, data/platform engineers, product owners) and define clear platform vs embedded team responsibilities.
Mentor and develop direct reports and senior technical leaders; drive hiring, performance management and succession planning.
Other Duties:
Serve as the executive sponsor for cross‑functional AI orchestration initiatives, representing the program to senior leadership and external stakeholders.
Own budget planning, forecasting, and spend optimization for the AI enablement portfolio; recommend investment priorities aligned to ROI.
Chair architecture reviews and change control boards for model, data and orchestration platform changes.
Maintain and enforce documentation standards (runbooks, SLOs/SLAs, playbooks, design artifacts) and ensure knowledge is distributed across teams.
Coordinate and support internal and external audits, regulatory inquiries, and compliance assessments related to AI and data usage.
Design and run enablement programs (training, labs, certifications) to scale skills across developers, data scientists, IT ops and business users.
Lead proof‑of‑value and pilot programs, translating results into scaled rollouts or decommissioning decisions.
Perform other duties as assigned to meet evolving business needs.
Qualifications
Required:
Bachelor’s degree in business, Engineering, Computer Science, Data Science, or related field.
Advanced degrees (MBA, M.S., or equivalent) strongly preferred.
8-10 years of related experience
Required knowledge
AI/ML fundamentals: supervised/unsupervised learning, large language models, fine‑tuning, evaluation metrics, bias and fairness concepts.
Orchestration & agentic patterns: multi‑model routing, agent chaining, prompt management, tool integrations, human‑in‑the‑loop designs and action policies.
Model serving & inference at scale: low‑latency routing, edge vs cloud tradeoffs, batching, autoscaling, cost‑aware routing and inference optimization.
Data engineering & feature management: feature stores, data pipelines, ETL, metadata, lineage and versioning.
Observability & reliability: telemetry design, drift detection, latency/cost monitoring, SLOs/SLAs, incident runbooks and MTTR practices.
Security & privacy: access controls, encryption, secrets management, secure model stores, data minimization, and secure data flows.
Integration & APIs: standards for connecting orchestration to ERP/CRM/WMS/TMS, RPA, third‑party services and carrier/3PL ecosystems.
Cost management & financial acumen: budgeting, P&L stewardship, cost/per‑inference modeling and ROI measurement.
Business domain fluency: ability to translate business processes into AI orchestration solutions and to define KPIs tied to operational outcomes.
Required skills
Technical depth: evaluate and make architecture decisions across ML infrastructure, orchestration, cloud/edge inference, and distributed systems.
Product and program management: prioritize a portfolio of initiatives, manage dependencies, run agile delivery, and track ROI and KPIs.
Vendor & commercial skills: evaluate vendors, negotiate contracts, and manage partner integrations and licensing.
Communication & stakeholder influence: present complex technical topics to executives, build consensus, and drive adoption across business teams.
People leadership: recruit, mentor and grow cross‑functional teams; manage performance and succession planning.
Operational rigor: define SLAs/SLOs, runbook‑driven incident response, observability standards, and continuous improvement loops.
Required abilities (behaviors & outcomes)
Translate complex business problems into concrete AI product solutions that deliver measurable efficiencies.
Build, mentor, and scale multidisciplinary teams and grow next-generation AI capability.
Make data-driven prioritization decisions and be accountable for delivery and financial outcomes.
Establish and enforce governance and lifecycle controls to ensure model trustworthiness and compliance.
Drive rapid experimentation while mitigating risk and ensuring safe production rollouts.
Identify capability gaps across leadership and teams and implement rotation, development, or role adjustments to sustain delivery.
Operate with sound judgment under ambiguity and coordinate cross-organizational change to convert insights into operational actions.
Johnson & Johnson is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, disability, protected veteran status or other characteristics protected by federal, state or local law. We actively seek qualified candidates who are protected veterans and individuals with disabilities as defined under VEVRAA and Section 503 of the Rehabilitation Act.
Johnson & Johnson is committed to providing an interview process that is inclusive of our applicants’ needs. If you are an individual with a disability and would like to request an accommodation, external applicants please contact us via https://www.jnj.com/contact-us/careers , internal employees contact AskGS to be directed to your accommodation resource.
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Required Skills:
Preferred Skills:
The anticipated base pay range for this position is :
$150,000.00 - $258,750.00Additional Description for Pay Transparency:
Subject to the terms of their respective plans, employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).Want to know if this job is worth applying to?