Director, Global Forward Deployed AI Programs

Johnson & Johnson Innovative Medicine•Raritan, NJ

9d•$164,000 - $282,900•Onsite

About The Position

The Director, Global Forward Deployed AI Programs will join Johnson & Johnson’s AI Strategy and Execution team in the Global Commercial Strategy Organization (GCSO). The role embeds with global Marketing, Medical Affairs, and Market Access teams to build and deliver production Generative AI and Agentic AI solutions addressing business needs. This global leadership position builds productized solutions for GCSO, influences regional AI teams, and establishes technical and operational standards for AI pilots and scaled solutions. The director will remain close to technical work, reviewing architectures, troubleshooting production issues, and collaborating with data scientists, data engineers, and advanced analytics professionals. They will define measures of success and coordinate AI product development and delivery across regions. The role includes building Forward Deployed Engineering capability, developing talent, establishing technical standards and operating cadences, and defining use cases and engagement success criteria. The director will review architectures, harnesses, evaluations, and code; prototype and debug solutions; monitor financial impact; and ensure compliance with regulatory, privacy, ethical, and medical/legal/regulatory requirements. They will create reusable playbooks and templates, influence platform and product roadmaps, assess vendors and platforms, and promote sustained AI adoption. The director will set standards for agent harnesses, establish evaluation processes and quality thresholds, and ensure ongoing testing as models, prompts, or data sources change. Working with Technology and platform teams, they will guide AI solutions from prototype through production, including CI/CD, staged rollouts, versioning, rollback plans, observability, and incident response. The position is based in Raritan, NJ or Horsham, PA, requires travel between those locations, and may involve travel to business partner and global sites up to 25%.

Requirements

Bachelor’s degree in Computer Science, Engineering, Operations Research, or a related field, or equivalent experience.
At least 7 years of experience in software engineering, ML/AI engineering, solutions architecture, or a technical client-facing role such as forward deployed engineering or technical consulting.
At least 5 years of people management experience leading engineers in a delivery, services, or embedded/Forward Deployed Engineering-style organization.
Track record of hiring, developing, and retaining strong technical talent.
Experience building a Forward Deployed Engineering capability from scratch.
Hands-on experience shipping production LLM applications and AI agents, including RAG architectures, tool use, multi-step orchestration, and prompt engineering.
Experience designing and running LLM evaluations, including building evaluation datasets, defining quality metrics, conducting automated and human-in-the-loop review, and using results to make release decisions.
Experience deploying and operating production AI systems, including CI/CD, monitoring and observability, cost and latency management, and enterprise security, privacy, and compliance requirements.
Experience working directly with business stakeholders on technical implementations and navigating complex organizational dynamics in a large enterprise.
Executive presence and ability to move between strategic discussions with senior leaders and hands-on debugging with engineers.
Strong written and verbal communication skills, including translating technical complexity into clear narratives for diverse audiences.
Ability to thrive in ambiguity and enthusiasm for building something new.

Nice To Haves

Master’s or PhD in a related field.
Pharma, healthcare, or regulated industry experience.
Familiarity with Salesforce, omnichannel marketing ecosystems, and field engagement workflows.

Responsibilities

Build the Forward Deployed Engineering competency and influence across GCSO and regional teams; partner with regions, R&D, and Supply Chain to develop and retain engineering talent.
Set technical standards and business-outcome and AI-adoption OKRs, and establish operating cadences such as standups, retrospectives, and engagement reviews.
Partner with the AI fluency leader to support technical skill building, particularly in agentic AI.
Define AI use cases, scope engagements, and align on success criteria before work begins.
Define and monitor financial impact metrics, including token costs and ROI of AI products.
Review technical architectures, harness designs, evaluation results, and code from internal teams and vendor partners; ensure solutions are reliable, production-ready, high-quality, and integrated into the AI ecosystem.
Lead technical discovery, prototype solutions, and debug complex issues alongside the team and vendor partners.
Ensure solutions meet regulatory, privacy, ethical, and medical/legal/regulatory standards, partnering with Technology, Privacy, and Compliance from design through deployment.
Create reusable playbooks, starter repositories, integration templates, and an internal knowledge base based on field experience.
Partner with Technology, Data Science, and Product teams to turn field insights into platform and product improvements; represent business needs in AI planning and roadmap discussions.
Support evaluation of AI vendors, model providers, and enterprise platforms, and contribute deployment experience to build-versus-buy decisions.
Champion AI adoption across GCSO and help business teams move from pilots to sustained use.
Define standards for agent harnesses, including orchestration patterns, tool and API integrations, context and memory management, guardrails, and human-in-the-loop checkpoints.
Establish evaluation practices for engagements, including task-specific evaluation sets, automated regression suites, LLM-as-judge and human review workflows, and release quality thresholds.
Ensure continuous evaluation of model upgrades, prompt changes, and new data sources against agreed benchmarks.
Own the path from prototype to production with Technology and platform teams, including CI/CD, staged rollouts, prompt and model versioning, and rollback plans.
Implement observability for deployed solutions, including tracing, cost and latency monitoring, quality and drift tracking, and incident response; feed production issues back into evaluations and harness improvements.
Build reusable harness components, evaluation frameworks, and deployment templates into team playbooks.

Benefits

Medical insurance
Dental insurance
Vision insurance
Life insurance
Short- and long-term disability insurance
Business accident insurance
Group legal insurance
Consolidated retirement plan (pension)
401(k) savings plan
Long-term incentive program
Vacation: 120 hours per calendar year
Sick time: 40 hours per calendar year, or 56 hours for employees residing in Washington
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 a child’s birth, adoption, or foster care placement
Condolence leave: 30 days for an immediate family member and 5 days for an extended family member
Caregiver leave: 10 days
Volunteer leave: 4 days
Military spouse time-off: 80 hours