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 work—guided 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 creation
• Technology Consulting – End to end technology advisory for clients, including developing technology roadmaps, platform/cloud/data advisory as well as transformation excellence for a digital transformation
• Data & 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-invention

How 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 applications—covering 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 environments
• Design platforms for model training, fine-tuning, high-availability inference, batch and event-driven pipelines, and long-running or agent-based workflows
• Ensure platforms are cloud-native, modular, extensible, and aligned with enterprise architecture standards
• Enable multi-cloud portability (Azure, AWS, GCP) through abstraction of cloud dependencies
• Partner with GenAI & Data Architects to align platform capabilities with RAG pipelines, agent orchestration, and data platform architectures

2. CI/CD & Automation

• Design and implement end-to-end CI/CD pipelines for applications, data pipelines, ML models, and GenAI prompts
• Standardize environment promotion with automated testing, approvals, rollback, and release controls
• Integrate pipelines with source control, artifact repositories, model registries, and prompt repositories
• Implement progressive delivery patterns such as blue-green deployments, canary releases, and feature flags
• Embed security scans, quality gates, and compliance checks directly into CI/CD workflows

3. Infrastructure as Code & Environment Standardization

• Define and enforce Infrastructure-as-Code standards using Terraform, ARM/Bicep, and cloud SDKs
• Automate provisioning of compute, storage, networking, Kubernetes clusters, and AI platform services
• Ensure environments are reproducible, version-controlled, auditable, and free from configuration drift

4. Observability, Reliability & SRE Practices

• Design and implement end-to-end observability including metrics, logs, and distributed tracing
• Define and monitor SLIs and SLOs for AI, data, and platform services
• Design for high availability, fault tolerance, and disaster recovery
• Lead incident response, root-cause analysis, and post-incident reviews
• Drive continuous reliability improvements using operational metrics

5. Cost Management & FinOps

• Implement FinOps practices for AI and data platforms
• Track and optimize infrastructure usage, cost per inference, and GenAI token consumption
• Establish cost guardrails including budgets, alerts, auto-scaling, and shutdown policies
• Partner with architects and business stakeholders to balance accuracy, latency, scale, and cost

6. Security, Governance & Compliance

• Embed security-by-design into platform architecture and delivery pipelines
• Implement IAM, secrets management, encryption, network segmentation, and secure connectivity
• Enable audit logging, traceability, and governance for model execution, prompt usage, and data access
• Support internal and external audits, penetration testing, and compliance reviews

7. MLOps / LLMOps Enablement

• Enable and operate MLOps and LLMOps platforms covering training, serving, monitoring, versioning, and rollback
• Support automated evaluation, retraining, drift detection, and performance degradation alerts
• Ensure platforms support experimentation without compromising production stability

8. Collaboration & Leadership

• Collaborate with GenAI & Data Architects, AI Engineers, Backend and Frontend Engineers, Security, QA, and Delivery teams
• Participate in Agile ceremonies, release planning, and roadmap discussions
• Provide technical leadership and mentoring to DevOps and platform engineers
• Define platform standards, documentation, and best practices
• Act as a trusted advisor to leadership on scalability, risk, and cost

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related discipline
• Master’s degree preferred
• Relevant certifications strongly desired (Azure/AWS/GCP Architect or DevOps, Kubernetes, Terraform)
• 8–12+ years of experience in DevOps, Platform Engineering, Cloud Infrastructure, or SRE roles