Director, PEPI - Technology Services CTO Domain

About the role

The PEPI Technology Services AI team [CTO Domain] at Alvarez & Marsal is seeking a Director to lead AI-driven value creation engagements across the private equity lifecycle, focusing on the software engineering, product development, and technical delivery organizations of PE-backed portfolio companies.

Responsibilities

• Own the full engagement lifecycle: scoping, workplanning, team management, executive communications, and financial delivery against defined engineering performance targets.
• Serve as primary point of contact for portfolio company CTOs, VPs of Engineering, and PE deal partners.
• Lead cross-functional engagement teams spanning software engineering, product management, DevOps, platform engineering, and data/ML.
• Drive executive-level workshops and steering committee presentations; translate engineering findings into financial narratives for PE audiences (R&D cost reduction, developer capacity uplift, time-to-market acceleration).
• Identify and prioritize AI use cases within the engineering and product organization mapped directly to EBITDA improvement, R&D cost reduction, and product growth.
• Lead AI-focused diligence workstreams: assess engineering organization AI maturity, SDLC efficiency, AI tooling adoption, DevOps posture, technical debt, and developer productivity to size value potential.
• Develop AI-native engineering operating models and SDLC transformation roadmaps with specific financial targets and DORA metric milestones.
• Quantify the financial value of AI-enabled engineering productivity: cost per feature, developer capacity freed, cycle time reduction, and deployment frequency uplift.
• Establish program governance, KPI frameworks, and value tracking tied to engineering metrics: DORA four key metrics, SPACE framework, AI tooling adoption rate, and engineering cost per feature.
• Manage vendor relationships, AI tooling providers, and platform implementation partners across timelines, budgets, and execution risks.
• Redesign core SDLC and DevOps processes to embed AI across the full engineering lifecycle: agentic code generation, automated PR review, AI-driven QA, intelligent deployment gates, and AI-augmented incident response.
• Align engineering organizational structures and talent models to support AI-augmented development at scale.
• Support business development: contribute to proposals, respond to PE firm RFPs, and participate in client pitches.
• Mentor Senior Associates and Analysts; contribute to A&M's Technology Services team methodology, tools, and accelerators for CTO domain engagements.

Required Skills & Technology Fluency

• Led or governed implementations using the technologies below—not merely advised on them.
• Design and operationalize AI governance frameworks for engineering organizations: AI acceptable use policies for code generation, AI-generated code review standards, license compliance for AI-suggested code, and security risk management for AI-authored software.
• Defined and implemented AI tooling adoption programs at scale: GitHub Copilot enterprise rollout (seat governance, usage analytics, ROI tracking), Cursor team deployments, Claude Code integration into CI/CD pipelines, and developer enablement programs.
• Conducted engineering AI maturity assessments: evaluated SDLC toolchain AI-readiness, DevOps automation depth, test coverage and quality gates, technical debt profile, and developer experience (DX) metrics.
• Navigated AI code security risks: SAST/DAST for AI-generated code, software bill of materials (SBOM) requirements, supply chain security, and AI-specific code vulnerability patterns.
• Lead enterprise deployments of AI coding tools: GitHub Copilot (Agent Mode, Copilot Coding Agent, multi-model selection), Cursor (rules configuration, codebase indexing, team policies), Claude Code (MCP server integration, agent workflows), Amazon Q Developer, or Codeium.
• Evaluate and govern autonomous coding agents in enterprise SDLC contexts: Devin, OpenAI Codex CLI, Google Antigravity—including quality gates, human-in-the-loop checkpoints, and guardrails against AI slop and hallucinated dependencies.
• Configure and govern AI code review platforms: CodeRabbit (organization-wide rules, PR summary policies), Qodo/CodiumAI (multi-agent review architecture, test generation), GitHub Copilot Code Review—including integration with existing code quality workflows.
• Measure and manage AI coding tool economics: token cost governance, premium request budgets (GitHub Copilot billing mechanics), developer adoption rates, and quality metrics (AI bug rate, rework time, hallucination frequency).
• Design prompt engineering standards for development teams: system prompt libraries, context injection patterns, retrieval-augmented code generation (using LangChain, LlamaIndex, or LangGraph), and codebase-aware LLM workflows.
• Lead platform engineering programs: internal developer platforms (IDPs) built on Backstage with AI plugin integrations, self-service infrastructure provisioning, golden path templates, and AI-accessible service catalogs (natural language queries via LLMs).
• Architect containerization and orchestration at scale: Kubernetes (EKS, AKS, GKE), Helm chart governance, service mesh (Istio, Linkerd), and operator patterns for AI workload deployment.
• Implement Infrastructure as Code governance programs: Terraform module libraries with AI-assisted generation (GitHub Copilot for IaC, Pulumi AI), Ansible playbooks, policy-as-code (OPA/Conftest, Checkov), and drift detection.
• Measure and improve engineering velocity: DORA four key metrics (deployment frequency, lead time for change, MTTR, change failure rate), SPACE framework, developer satisfaction surveys, and AI uplift quantification using LinearB, Jellyfish, Waydev, or Swarmia.
• Design data pipelines for ML feature engineering: Apache Spark, dbt, Airflow, Prefect—and architect feature stores (Feast, Tecton, Databricks Feature Store) that support both batch and real-time model serving.
• Implement cloud-native engineering environments for AI-intensive workloads: GPU/accelerator compute (AWS P/Trn instances, Azure NDv5/NCv3, GCP A100/H100), spot/preemptible instance strategies, and multi-region inference serving architectures.
• Implement observability stacks for engineering performance: Datadog APM, Dynatrace full-stack, Grafana/Prometheus (including AI-assisted alert definition), PagerDuty AIOps—with SLO/SLA tracking and AI-augmented incident response.
• Lead DevSecOps programs: SAST (Semgrep, Checkmarx, SonarQube), DAST (OWASP ZAP, Burp Suite), container security (Trivy, Snyk, Aqua), and AI-generated code risk scanning pipelines integrated into CI/CD.
• Lead engineering cloud cost governance: FinOps practices, rightsizing AI compute, reserved instance strategies, and engineering cost per feature modeling tied to PE value creation targets.
• Build engineering economics: build engineering economics models, including cost per feature, developer capacity freed, cycle time reduction, and deployment frequency uplift.