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Description About Alvarez & Marsal Alvarez & Marsal (A&M) is a global consulting firm with over 10,000 entrepreneurial, action and results-oriented professionals in over 40 countries. We take a hands-on approach to solving our clients' problems and assisting them in reaching their potential. Our culture celebrates independent thinkers and doers who positively impact our clients and shape our industry. The collaborative environment and engaging workguided by A&M's core values of Integrity, Quality, Objectivity, Fun, Personal Reward, and Inclusive Diversity - are why our people love working at A&M. The Team Our DTS team covers the full breadth of Technology Consulting and M&A services, including - Technology M&A and Strategy - Assist clients to manage the technology aspects and business enablement of complex M&A, integrations and carve-outs as well as post-deal value creationTechnology Consulting End to end technology advisory for clients, including developing technology roadmaps, platform/cloud/data advisory as well as transformation excellence for a digital transformationData & AI services - Helping clients in harnessing the power of data and cutting-edge analytics to drive intelligent decision-making and transform businesses.Develop GenAI and Agentic AI solutions that create real business value for clients through process re-inventionHow you will contribute TheLead / DevOps Platform Engineeris afoundational roleresponsible for enablingreliable, secure, scalable, and cost-governed delivery of AI, Machine Learning, and Generative AI solutionsacross the enterprise.This role owns theplatform layer that sits beneath AI applicationscovering cloud infrastructure, CI/CD pipelines, MLOps/LLMOps automation, observability, security, and cost controls. The role ensures that AI solutions do not remain experimental but areproduction-ready, repeatable, auditable, and operable at scale.This role exists toeliminate risksand provide astable platform backbonefor AI and data teams to innovate safely and efficiently.Key Responsibilities 1. AI Platform & Cloud Architecture Own and evolve cloud platform architecture supporting AI, ML, and GenAI workloads across all environmentsDesign platforms for model training, fine-tuning, high-availability inference, batch and event-driven pipelines, and long-running or agent-based workflowsEnsure platforms are cloud-native, modular, extensible, and aligned with enterprise architecture standardsEnable multi-cloud portability (Azure, AWS, GCP) through abstraction of cloud dependenciesPartner with GenAI & Data Architects to align platform capabilities with RAG pipelines, agent orchestration, and data platform architectures2. CI/CD & Automation Design and implement end-to-end CI/CD pipelines for applications, data pipelines, ML models, and GenAI promptsStandardize environment promotion with automated testing, approvals, rollback, and release controlsIntegrate pipelines with source control, artifact repositories, model registries, and prompt repositoriesImplement progressive delivery patterns such as blue-green deployments, canary releases, and feature flagsEmbed security scans, quality gates, and compliance checks directly into CI/CD workflows3. Infrastructure as Code & Environment Standardization Define and enforce Infrastructure-as-Code standards using Terraform, ARM/Bicep, and cloud SDKsAutomate provisioning of compute, storage, networking, Kubernetes clusters, and AI platform servicesEnsure environments are reproducible, version-controlled, auditable, and free from configuration drift4. Observability, Reliability & SRE Practices Design and implement end-to-end observability including metrics, logs, and distributed tracingDefine and monitor SLIs and SLOs for AI, data, and platform servicesDesign for high availability, fault tolerance, and disaster recoveryLead incident response, root-cause analysis, and post-incident reviewsDrive continuous reliability improvements using operational metrics5. Cost Management & FinOps Implement FinOps practices for AI and data platformsTrack and optimize infrastructure usage, cost per inference, and GenAI token consumptionEstablish cost guardrails including budgets, alerts, auto-scaling, and shutdown policiesPartner with architects and business stakeholders to balance accuracy, latency, scale, and cost6. Security, Governance & Compliance Embed security-by-design into platform architecture and delivery pipelinesImplement IAM, secrets management, encryption, network segmentation, and secure connectivityEnable audit logging, traceability, and governance for model execution, prompt usage, and data accessSupport internal and external audits, penetration testing, and compliance reviews7. MLOps / LLMOps Enablement Enable and operate MLOps and LLMOps platforms covering training, serving, monitoring, versioning, and rollbackSupport automated evaluation, retraining, drift detection, and performance degradation alertsEnsure platforms De