AI Platform Engineer (MLOps Engineer)
About the CompanyWe are hiring AI Platform Engineer (MLOps Engineer). Please share resumes at Akanksha@mcubesoft.comAbout the RoleAI Platform Engineer (MLOps Engineer), Legal Operations. The Applied Data Science team within Legal Operations builds AI-powered tools, analytics capabilities, and data infrastructure that enable Apple's legal organization to work smarter and faster. The team uses Apple's internal AI platform to build and deploy production-grade AI applications — from document analysis and legal research to spend analytics and conversational intelligence. As the volume and complexity of AI deployments grows, the team needs a dedicated engineering discipline to own the path from prototype to production. The MLOps Engineer is the bridge between the AI applications the Applied Data Science team builds and the production environment where stakeholders rely on them. You will own deployment pipelines, integration infrastructure, access governance, and scalability for every AI-powered tool the team ships. This is not a model training or data science role — it is an engineering role focused on making AI applications reliable, governed, and scalable in an enterprise environment.Location: Sunnyvale, CA or Austin, TX (Hybrid 3 days On-Site and 2 days Remote)Duration: 6-12 MonthsResponsibilitiesDeployment & Delivery Own CI/CD pipelines for AI-powered applications — from development environments through production releaseBuild and maintain reusable deployment templates that enable the team to ship AI tools faster and more consistentlyManage environment promotion — dev → staging → production — with appropriate testing and validation gates at each stageCoordinate with infrastructure and platform teams on deployment standards and security requirementsIntegration & Data Connectivity Own API integrations connecting AI applications to live Legal data sourcesManage live data refresh pipelines, ensuring AI tools reflect current data without manual interventionVersion and manage API contracts — handling changes in upstream data sources without breaking downstream AI applicationsTroubleshoot and resolve integration failures with minimal impact to end usersGovernance & Security Implement role-based access control (RBAC) for all deployed AI applications — ensuring the right stakeholders have access to the right toolsBuild and maintain audit logging — capturing usage, queries, and responses for compliance and accountabilityEnsure all deployments meet Apple's enterprise security standards — secrets management, authentication, data handling policiesPartner with governance and data teams to enforce data access policies at the application layerObservability & Reliability Instrument deployed AI applications with monitoring for latency, error rates, token usage, and response qualityBuild alerting for production failures and performance degradationEstablish SLAs for AI application uptime and response time; own resolution when they are breachedMaintain deployment documentation and runbooks for every production applicationEnablement Reduce the deployment burden on data scientists and AI engineers — abstract complexity so builders can focus on buildingBuild and maintain developer tooling that makes the path from prototype to production faster and more self-service over time QualificationsMinimum Qualifications 3+ years of experience in MLOps, DevOps, or platform engineering rolesStrong Python skills — this is the primary language for tooling and automationHands-on experience deploying and operating LLM-powered applications in productionExperience building CI/CD pipelines for AI or software applications (GitHub Actions or equivalent)Experience with REST API development and integration — connecting applications to live data sourcesWorking knowledge of containerization — Docker; Kubernetes basicsExperience implementing access control, authentication, and audit logging in enterprise environmentsStrong communication skills — able to work across data science, engineering, and infrastructure teams Preferred Qualifications Experience with LLM observability tooling — monitoring latency, token usage, response quality in productionFamiliarity with dbt, Snowflake, or enterprise data warehouse environmentsExperience with vector database management — Chroma, Pinecone, Weaviate, or equivalentExperience in a regulated or compliance-sensitive environment where auditability and data access governance are non-negotiableFamiliarity with RAG (Retrieval-Augmented Generation) application architecture — not to build them, but to deploy and operate them reliablyExperience building reusable deployment frameworks, not just one-off deployments