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Johnson & Johnson
Johnson & Johnson
H1B Visa SponsorHealthcareHealthcare InsurancePharmaceuticals
Johnson & Johnson is a global healthcare company working across innovative medicine and medical technology. Its teams develop solutions in oncology, immunology, neuroscience, specialty ophthalmology, interventional care, and orthopaedics, combining science and technology to address complex health needs. The company also emphasizes health equity, environmental sustainability, diversity, and inclusion as it serves providers, patients, and communities worldwide.
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Lead AI Operations Engineer - Milan Hybrid

Lead AI Operations Engineer responsible for secure, observable, cost-efficient AI operations at Johnson & Johnson. The role supports production AI applications across Milan, Madrid, and Lisbon.

ITItaly
€48,600 – €77,395 / year
HybridFull-timeSeniorEnglish RequiredAI EngineerMachine Learning Engineer
AzureCloudCyber SecurityPythonTerraform

Description

  • Design and deliver comprehensive observability for AI applications and agents
  • Standardize telemetry and distributed tracing across agent workflows, APIs, data services, vector stores, model endpoints, and external tools
  • Develop dashboards and alerts covering availability, latency, errors, reliability, quality, policy violations, consumption, and service health
  • Establish service-level indicators, objectives, error budgets, alert thresholds, and operational-readiness standards
  • Support trace-based debugging, incident reconstruction, and controlled session replay while safeguarding sensitive data
  • Track retrieval quality, data freshness, model and prompt regressions, abnormal agent loops, tool degradation, and other unexpected runtime behavior
  • Lead root-cause investigations for AI platform and runtime incidents, then implement preventive improvements and controls
  • Build reusable pipelines, templates, and controls for versioning configurations, prompts, models, and agent components
  • Introduce automated deployment gates, integration testing, regression validation, security checks, and observability-coverage checks
  • Support canary deployments, feature flags, model and provider routing, fallback options, and rollback procedures
  • Develop operational tooling for multiple models, frameworks, cloud environments, and agent architectures
  • Maintain runbooks, reference implementations, engineering standards, and production-support practices
  • Implement metering, allocation, showback, budgets, thresholds, anomaly alerts, and runtime controls for AI consumption
  • Work with product teams to optimize model choice, routing, context size, caching, batching, retries, and tool usage
  • Integrate security, privacy, Responsible AI, compliance, identity, access management, secrets handling, and auditability into the AI runtime
  • Develop protections against prompt injection, jailbreaks, unauthorized tool use, excessive permissions, data leakage, unsafe execution paths, and anomalous access
  • Define operating procedures for monitoring, support, incidents, problems, changes, escalations, and service recovery
  • Create production-readiness checklists, acceptance criteria, on-call runbooks, escalation routes, recovery procedures, and business-continuity requirements
  • Automate operational processes to reduce reconstruction work and accelerate detection, diagnosis, containment, and recovery
  • Prepare technical documentation and coach engineering teams in approved operational practices
  • Connect AI, platform, cloud, data, architecture, cybersecurity, privacy, compliance, and service management teams as a technical liaison
  • Define and track KPIs for reliability, observability, incident performance, cost efficiency, control coverage, audit evidence, and platform adoption
  • Use operational data to prioritize automation, reliability gains, cost optimization, and risk reduction

Requirements

  • Bachelor’s or master’s degree in computer science, engineering, artificial intelligence, or a related field, or equivalent relevant experience
  • At least five years of hands-on experience in platform engineering, site reliability engineering, DevOps, DevSecOps, MLOps, or production software engineering with live-service ownership
  • Practical experience running machine learning, generative AI, or agentic systems in production
  • Hands-on proficiency with Python, APIs, infrastructure as code, and CI/CD tools such as Terraform, GitHub Actions, or Azure DevOps
  • Experience with cloud-native platforms, containers, orchestration, and enterprise cloud services; Azure experience is strongly valued
  • Experience implementing observability through logs, metrics, distributed traces, dashboards, and alerts; OpenTelemetry or comparable standards are preferred
  • Strong understanding of LLM application patterns, including RAG, vector stores, model gateways, tool calling, agent memory, and multi-agent orchestration
  • Working knowledge of identity and access management, secrets management, secure logging, threat modeling, privacy by design, and audit controls
  • Ability to convert ambiguous operational and control needs into reusable technical capabilities, standards, automation, and runbooks
  • Strong stakeholder management, technical communication, and documentation abilities
  • Experience in regulated industries with formal security, privacy, quality, validation, risk, or audit requirements is preferred
  • Hands-on experience with Azure OpenAI, Azure AI Foundry, Azure Monitor, Application Insights, Microsoft Fabric, or comparable cloud services is preferred
  • Experience with AI observability or evaluation platforms such as Langfuse, LangSmith, Arize, MLflow, or equivalent tools is preferred
  • Experience operating multiple model providers, multi-cloud services, or agent frameworks is preferred
  • Knowledge of FinOps, cloud cost allocation, budgeting, and AI workload unit-economics measurement is preferred
  • Experience defining service-level objectives, on-call models, incident management, and operational-readiness gates for enterprise platforms is preferred

Benefits

  • Annual bonus with a target determined by pay grade and location, based on individual and company performance
  • Vacation entitlement
  • At least 12 weeks of parental leave
  • Bereavement leave
  • Caregiver leave
  • Volunteer leave
  • Well-being reimbursement
  • Financial, physical, and mental health programs
  • Service-anniversary and recognition awards
  • Insurance plans for employees and, in some locations, eligible dependents

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