IT Sr. Manager MLL AI Leader

Johnson & Johnson Innovative Medicine•Raritan, NJ

20h

About The Position

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at jnj.com As guided by Our Credo, Johnson & Johnson is responsible to our employees who work with us throughout the world. We provide an inclusive work environment where each person is considered as an individual. At Johnson & Johnson, we respect the diversity and dignity of our employees and recognize their merit. Job Description: Position Summary We are seeking a Senior Manager to drive Generative AI (GenAI), Machine Learning (ML), and Intelligent Automation initiatives across our global supply chain operations (Plan, Make, Deliver). This hands-on leader will spearhead the technical implementation and delivery of AI solutions, leveraging large language models (LLMs) and automation technologies to optimize supply chain processes. The ideal candidate combines deep expertise in GenAI platforms and LLMs with strong supply chain domain knowledge to guide a large, globally distributed team new to AI, accelerating AI adoption at scale. In this role, you will partner with our AI Community of Practice and other technology teams to deliver transformative solutions, while ensuring robust governance and upskilling of the broader organization in AI. Why This Role is Exciting In this position, you will play a pivotal role in blending cutting-edge AI technology with core supply chain operations to drive efficiency and innovation. You’ll lead high-impact projects – for example, deploying generative AI agents to assist planners in scenario planning, using machine learning to predict and prevent supply disruptions, or automating routine tasks to free our teams for higher-value work. As the organization’s GenAI and Intelligent Automation champion, you will not only deliver solutions but also elevate the AI fluency of a large team, leaving a lasting capability. Join us if you are passionate about transforming traditional supply chain functions with AI, enjoy working at the intersection of technology and business, and are ready to guide a global team through the next wave of digital innovation. Together, we will build a smarter, more resilient supply chain powered by AI.

Requirements

Bachelor’s degree in Complutre Science, Engineering or related field/Masters/Ph.D. in Computer Science, Machine Learning, AI, Data Science, Engineering, or related field.
8+ years of progressive experience in applying AI/ML to real-world problems, including at least 305 years leading projects or teamsin developing AI-driven solutions. Proven track record of delivering production-grade, enterprise-scale AI/ML applications.
Solution architecture & development: Ability to design scalable AI architectures (e.g. integrating LLM APIs, vector databases for Retrieval-Augmented Generation) and to code or review code for AI solutions. Project management: Proven skill in managing full lifecycle of AI projects from ideation through deployment and iteration, coordinating with data engineers, developers, and business stakeholders to deliver on-time.
MLOps & quality focus: Experience implementing MLOps practices (CI/CD for ML, model monitoring, A/B testing) and LLM Ops for large models.
Knowledge of AI risk management and Responsible AI (bias mitigation, data privacy, model compliance) to ensure solutions meet corporate governance and industry regulations.
Analytical mindset: Strong ability to define relevant performance metrics (e.g. forecast error, service level uplift) and analyze outcomes for improvement
Stakeholder engagement: Excellent communication skills to translate complex AI concepts into business terms and to rally cross-functional teams around new AI initiatives.
Change management: Ability to drive user adoption by demonstrating value, delivering training, and providing user support for new AI tools. Familiarity with enterprise platforms (SAP, Oracle, etc.) for seamless integration of AI solutions into workflows.
Leadership & coaching: Demonstrated ability to mentor technical teams and non-experts, explaining AI techniques and enabling others to contribute (e.g. through code reviews, workshops.
Strategic vision: Forward-looking mindset to build the organization’s AI maturity – staying up-to-date with the latest GenAI research and tools, and identifying how to apply them for
business benefit. Strong interpersonal skills to inspire and lead in a global, matrixed environment.
Programming & Data: Strong programming skills in Python (or similar) for AI/ML development. Proficient in ML libraries/frameworks (TensorFlow, PyTorch, scikit-learn) and NLP libraries. Solid SQL and data handling skills for large datasets.
Cloud & MLOps: Experience deploying AI solutions on cloud platforms (Azure, AWS, or GCP) using managed AI/ML services. Familiar with containerization (Docker, Kubernetes) and CI/CD pipelines for ML. Experience with MLOps tools (model tracking, automated retraining) and LLMOps for managing LLM deployments (performance tuning, cost optimization).
Automation Tools: Working knowledge of Intelligent Automation technologies – e.g. Robotic Process Automation (RPA) tools, workflow orchestration, or low-code automation – to integrate AI into end-to-end process automation.
Supply Chain Domain Knowledge: Strong understanding of supply chain operations in Plan, Make, Deliver functions. Familiarity with processes such as demand/supply planning (S&OP), production scheduling, inventory management, logistics and order fulfillment40. Able to translate supply chain challenges into AI use cases (e.g. using ML for demand forecasting, using computer vision for quality, using NLP for logistics issue triage). Knowledge of supply chain systems (ERP like SAP, MES, WMS, TMS) and data structures is a must to ensure AI solutions integrate well.
Leadership & Communication: Demonstrated ability to lead cross-functional teams or initiatives. Excellent communicator able to articulate complex AI concepts to non-technical stakeholders and senior leadership. Experience driving change or innovation program in a large organization, with strong influencing skills. Comfortable working in a global team environment and adapting communcition across culture and functions.
GenAI/LLM Expertise: Deep hands-on experience with large language models and generative AI platforms. Familiarity with transformer architectures and NLP techniques (e.g. GPT, BERT), prompt engineering, fine-tuning and evaluating LLMs. Experience building LLM-powered applications (chatbots, agents, knowledge search) and using frameworks like LangChain or similar for agent orchestration.

Nice To Haves

Global/Enterprise Experience: Experience in a large global company or consulting firm in a role driving digital innovation or analytics in supply chain or manufacturing. Understanding of the complexities of scaling solutions across diverse business units and regions.
Advanced AI Techniques: Experience with advanced GenAI techniques such as fine-tuning large models on proprietary data, building Retrieval-Augmented Generation (RAG) systems (combining LLMs with vector databases/knowledge graphs), or developing multi-agent AI systems. Familiarity with vector databases (e.g. Qdrant, Pinecone) and semantic search in an enterprise context is a plus.
Supply Chain Analytics: Background in supply chain analytics or optimization (e.g. demand forecasting, network optimization, inventory optimization) using AI/OR techniques. Knowledge of specialized supply chain software (e.g. Kinaxis, Blue Yonder) and how AI can augment these tools.
Intelligent Automation: Hands-on experience implementing intelligent automation solutions such as RPA, process mining, or IoT/edge analytics in an operations context. Ability to combine rule-based automation with AI (for example, using ML to handle exceptions in an automated process).
Responsible AI & Compliance: Familiarity with regulated industries (pharmaceutical, medical devices, etc.) and their compliance requirements is a plus. Experience implementing AI under data privacy and security constraints. Knowledge of corporate governance processes for approving AI use cases (e.g. AI councils, model validation committees) is advantageous.
Certifications: Relevant certifications such as AWS/Azure AI Engineer, CSCP or supply chain certifications, or Six Sigma/Lean for process improvement. Any GenAI specializations or courses demonstrating continuous learning in this fast-evolving field.

Responsibilities

Lead AI Solution Development: Identify and implement GenAI/ML solutions to improve supply chain planning,manufacturing, and delivery processes
Technical Leadership & Delivery: Oversee the design, build, testing, and rollout of AI software components and automation workflows for supply chain use cases.This includes leveraging LLMs (for insights, predictions, or conversational agents) and intelligent automation (RPA, bots) to streamline processes.
AI Governance & Best Practices: Establish robust governance for AI/ML models and automation tools – including MLOps/LLM Ops pipelines, evaluation metrics, and compliance with responsible AI guidelines. Monitor model performance post-deployment and drive continuous improvements and retraining as needed.
Cross-Functional Collaboration: Work closely with supply chain business units and IT teams to identify automation opportunities, gather requirements, and integrate AI solutions into existing processes/systems. Partner with enterprise AI teams (e.g. Data Science,Center of Excellence) and external vendors as needed to accelerate implementation.
Mentoring & Capability Building: Serve as the in-house GenAI/ML expert and coach – upskilling the broader team in AI. Lead training sessions, share best practices, and guide junior engineers or citizen developers working on automation. Champion a culture of data-driven innovation within the MLL organization supporting supply chain product groups.
Fairness, Ethics & Compliance: Define and implement responsible AI practices that are safe, transparent, compliant and secure, for JnJ.Implement audit trails, controls, and ethical guardrails for agentic AI systems.
Define OKRs to measure value from AI realization across MLL applications, solutions and platforms.

Benefits

employees are eligible to participate in the Company’s consolidated retirement plan (pension) and savings plan (401(k)).
Vacation –120 hours per calendar year
Sick time - 40 hours per calendar year; for employees who reside in the State of Colorado –48 hours per calendar year; for employees who reside in the State of Washington –56 hours per calendar year
Holiday pay, including Floating Holidays –13 days per calendar year
Work, Personal and Family Time - up to 40 hours per calendar year
Parental Leave – 480 hours within one year of the birth/adoption/foster care of a child
Bereavement Leave – 240 hours for an immediate family member: 40 hours for an extended family member per calendar year
Caregiver Leave – 80 hours in a 52-week rolling period
Volunteer Leave – 32 hours per calendar year
Military Spouse Time-Off – 80 hours per calendar year