Lead AI Operations Engineer

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.

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Job Description:

We are recruiting for a Lead AI Operations Engineer based in Milan - Italy ; Madrid ; Spain or Lisbon ; Portugal

The AI Operations Engineer is responsible for shaping, designing, implementing, and continuously improving the enterprise capabilities required to operate AI applications and AI agents safely, reliably, transparently, and cost-effectively at scale. The role combines hands-on AI platform engineering with Site Reliability Engineering, DevSecOps, LLMOps, AgentOps, FinOps, security, and compliance practices.

The AI Operations Engineer builds reusable operational capabilities across observability, runtime controls, cost management, auditability, incident response, and production support. The role works closely with the Agent Factory, AI Engineering, Data Platforms, Cloud Infrastructure, Cybersecurity, Privacy, Risk, Quality, and Responsible AI stakeholders.

The role does not own the end-to-end lifecycle management of agents or AI products. Agent design, development, functional evaluation, release content, product evolution, and retirement decisions remain with the Agent Factory and the relevant AI product teams. The AI Operations Engineer provides the shared operational platform, telemetry, controls, and guardrails that enable those teams to run AI solutions in production.

Key Responsibilities

AI Observability & Production Reliability

• Design and implement end-to-end observability for AI applications and agents, including prompts, responses, model calls, tool calls, retrieval steps, decision paths, latency, failures, token consumption, and session context.
• Establish common telemetry and distributed tracing across agent workflows, APIs, data services, vector stores, model endpoints, and external tools.
• Build operational dashboards and alerts covering availability, latency, errors, reliability, quality signals, policy violations, consumption, and service health.
• Define service-level indicators, service-level objectives, error budgets, alert thresholds, and operational readiness criteria for production AI services.
• Enable trace-based debugging, incident reconstruction, and controlled session replay while protecting confidential or sensitive information in logs.
• Monitor retrieval quality, data freshness, model and prompt regressions, anomalous agent loops, degraded tool performance, and unexpected runtime behavior.
• Lead technical root-cause analysis for AI platform and runtime incidents and convert findings into preventive controls, automation, and engineering improvements.

LLMOps & AgentOps Platform Enablement

• Engineer reusable pipelines, templates, and controls for configuration, prompt, model, and agent-component versioning across environments.
• Implement automated technical gates for deployment readiness, including integration tests, regression checks, operational validation, security checks, and observability coverage.
• Enable controlled rollout patterns such as canary releases, feature flags, model or provider routing, fallback strategies, and technical rollback mechanisms.
• Provide common operational tooling that supports multiple models, frameworks, clouds, and agent patterns without creating a separate operating process for each solution.
• Integrate functional evaluation signals supplied by the Agent Factory or AI product teams into deployment gates and runtime monitoring, while functional quality ownership remains with those teams.
• Maintain reusable runbooks, reference implementations, engineering standards, and paved-road patterns for production operation and support.

AI FinOps & Consumption Efficiency

• Create transparent metering, allocation, and showback capabilities by product, agent, workflow, model, environment, and business unit where the required identifiers are available.
• Monitor token consumption, model utilization, repeated or runaway loops, retrieval overhead, infrastructure usage, and cost per successful transaction or workflow.
• Implement budgets, thresholds, anomaly alerts, and runtime guardrails to detect and contain unexpected consumption.
• Partner with Agent Factory and product teams to optimize model selection, routing, context size, caching, batching, retries, and tool usage while preserving agreed quality and compliance requirements.
• Define operational unit economics and provide evidence for capacity planning, optimization priorities, and platform investment decisions.

Security, Governance & Compliance Engineering

• Embed security, privacy, Responsible AI, and compliance controls into the shared AI runtime and operational toolchain in line with enterprise policies and approved risk frameworks.
• Implement identity, role-based access control, least privilege, managed identities, secrets management, and segregation of duties for agents, tools, services, and operators.
• Engineer runtime controls for prompt injection, jailbreak attempts, unauthorized tool use, excessive permissions, data leakage, unsafe execution paths, and anomalous access patterns.
• Design privacy-aware logging, retention, redaction, and access patterns for prompts, responses, memory, traces, and audit evidence.
• Provide auditable records linking versions, configurations, identities, actions, approvals, policy decisions, and operational outcomes.
• Automate policy checks and evidence collection where feasible, partnering with Cybersecurity, Privacy, Quality, Legal, Risk, and Responsible AI stakeholders for control definition and approval.
• Support threat modelling, security testing, incident response, remediation, and continuous control improvement for the AI platform.

Operational Service Management & Enablement

• Define operating processes for monitoring, support, incident management, problem management, change management, escalation, and service recovery.
• Create production-readiness checklists, service acceptance criteria, on-call runbooks, escalation paths, recovery procedures, and business-continuity requirements.
• Clarify operational handoffs and accountability across AI Operations, the Agent Factory, AI product teams, and underlying platform owners.
• Drive automation that reduces manual reconstruction of agent behavior and shortens detection, diagnosis, containment, and recovery times.
• Produce clear technical documentation and enable engineering teams to adopt approved operational patterns through practical coaching and reusable examples.

Stakeholder Management

• Act as the technical bridge between the Agent Factory, AI product teams, platform engineering, cloud infrastructure, data platforms, architecture, cybersecurity, privacy, compliance, and service management functions.
• Translate operational, risk, and control requirements into implementable technical capabilities and platform standards.
• Influence engineering teams to adopt common observability, reliability, cost, security, and compliance patterns.

Governance, Risk & Compliance

• Ensure shared AI Operations capabilities support applicable enterprise requirements for security, privacy, Responsible AI, auditability, and regulatory compliance.
• Maintain traceability of operational controls, exceptions, evidence, ownership, and remediation actions.

Metrics & Continuous Improvement

• Define and monitor KPIs for service reliability, observability coverage, incident performance, cost efficiency, control coverage, audit evidence, and platform adoption.
• Use operational evidence to prioritize automation, reliability improvements, cost optimization, and risk reduction.
• Measure the proportion of production AI services covered by approved telemetry, alerts, runbooks, cost controls, and security guardrails.

Qualifications

Required

• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related discipline, or equivalent relevant experience.
• 5+ years of hands-on experience in Platform Engineering, Site Reliability Engineering, DevOps, DevSecOps, MLOps, or production software engineering, including responsibility for live services.
• Practical experience operating Machine Learning, Generative AI, or agentic systems in production, beyond proofs of concept or prompt engineering.
• Hands-on capability in Python, APIs, infrastructure as code, and CI/CD practices using technologies such as Terraform, GitHub Actions, or Azure DevOps.
• Experience with cloud-native platforms, containers, orchestration, and enterprise cloud services, with Azure experience strongly valued.
• Experience implementing observability using logs, metrics, distributed traces, dashboards, and alerts, ideally using OpenTelemetry or equivalent standards.
• 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 modelling, privacy by design, and audit controls.
• Ability to turn ambiguous operational and control requirements into reusable technical capabilities, standards, automation, and runbooks.
• Strong stakeholder management, technical communication, and documentation skills.

Preferred

• Experience working in regulated industries with formal security, privacy, quality, validation, risk, or audit requirements.
• Hands-on experience with Azure OpenAI, Azure AI Foundry, Azure Monitor, Application Insights, Microsoft Fabric, or comparable cloud services.
• Experience with AI observability or evaluation platforms such as Langfuse, LangSmith, Arize, MLflow, or equivalent technologies.
• Experience operating heterogeneous model providers, multi-cloud services, or multiple agent frameworks.
• Knowledge of FinOps practices, cloud cost allocation, budgeting, and unit-economics measurement for AI workloads.
• Experience defining service-level objectives, on-call models, incident management, and operational-readiness gates for enterprise platforms.
  
  

Required Skills:

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The anticipated base pay range for this position is:

Benefits:

In addition to base pay, we offer the following benefits*: an annual bonus with set target (% of pay) depending on pay grade / location, where the actual amount is based on the employees’ and companies’ performance of the previous calendar year, or sales commissions. Moreover, we offer vacation days, parental leave for a minimum of 12 weeks, bereavement leave, caregiver leave, volunteer leave, well-being reimbursement, programs for financial, physical and mental health. We also offer service anniversary and recognition awards, and subject to the terms of their respective plans, employees - and in some location’s eligible dependents - can participate in several insurance plans. For more information, visit Employee benefits | Supporting well-being & career growth | Johnson & Johnson Careers.

*This is for informative purposes only. Amounts and actual benefits may vary by location and are subject to change.