Platform Engineer Backstage Jobs in Philadelphia, PA

Job Description: 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. A&M's Private Equity Performance Improvement Services (PEPI) practice focuses on serving upper middle market and large cap private equity firms who have engaged A&M to help improve operating results at their portfolio companies ($50M–$1B+ revenue range). Our PEPI services include: IT / Product / Engineering [Technology Services] CDD/Strategy Interim Management Merger Integration & Carve-outs Rapid Results Supply Chain CFO Services Technology Services – CTO Domain The Technology Services team works directly with private equity firms and their portfolio companies to drive measurable value through applied AI. Within the CTO Domain, our engagements target the software engineering, product development, and technical delivery organizations of portfolio companies—helping them embed AI across the SDLC, DevOps pipeline, and product lifecycle to increase engineering throughput, reduce R&D cost, and accelerate time-to-market. Specific focus areas include: AI-native SDLC transformation: embedding agentic AI coding tools and assistants across requirements, design, code generation, code review, testing, and release management Engineering productivity measurement and uplift: DORA metrics improvement, developer capacity modeling, and AI tooling ROI quantification DevOps and CI/CD pipeline modernization incorporating AI-driven automation, intelligent testing, and deployment orchestration MLOps and AI model lifecycle management: model versioning, CI/CD for ML, monitoring, drift detection, and responsible AI guardrails Platform engineering: AI-accessible developer platforms, self-service internal tooling, and Backstage-based developer portals with AI integrations Technical debt assessment and architecture modernization to enable AI-ready, cloud-native engineering environments AI-enabled product roadmap acceleration: using AI to shorten development cycles, increase feature velocity, and reduce cost per feature delivered End-to-end transformation execution, governance, and value tracking tied to engineering financial targets We align AI initiatives with investment theses, financial targets, and operational realities to ensure durable impact. Role Overview As a Director in the PEPI Technology Services AI team [CTO Domain], you will independently lead AI-driven value creation engagements across the private equity lifecycle, with a specific focus on the software engineering, product development, and technical delivery organizations of PE-backed portfolio companies. You own scope, workplan, client relationships, and financial outcomes end-to-end. This role requires a practitioner who has operated inside or directly alongside engineering and product organizations—someone who has personally led SDLC transformations, managed engineering teams, governed AI coding tool rollouts, or run DevOps programs at scale—and who can translate that hands-on operating experience into rapid, credible impact within the compressed timelines of a PE holding period. How You Will Contribute Engagement Leadership & Client Ownership 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) AI Strategy, Diligence & Value Creation 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 Transformation Execution & Governance 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 Practice Contribution 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 Directors are expected to have led or governed implementations using the technologies below—not merely advised on them. Candidates should be able to speak to specific engineering programs they have run, financial outcomes they delivered, and technical decisions they owned. AI Strategy & Governance within the Engineering Organization 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 Define and implement 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 Build AI tooling ROI models: baseline developer productivity (DORA metrics, cycle time, story point velocity), measure AI-driven uplift, and translate into EBITDA-relevant engineering cost reduction narratives for PE audiences Conduct engineering AI maturity assessments: evaluate SDLC toolchain AI-readiness, DevOps automation depth, test coverage and quality gates, technical debt profile, and developer experience (DX) metrics Navigate 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 AI Coding Tools & Agentic Software Development 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 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 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) DevOps, CI/CD & Platform Engineering Design and govern AI-augmented CI/CD pipelines: GitHub Actions (including Copilot Coding Agent PR automation), GitLab CI/CD, Azure DevOps Pipelines—with AI-driven intelligent test selection, automated deployment gates, and self-healing pipeline logic 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 MLOps & AI Model Lifecycle Management Design and govern end-to-end MLOps platforms: MLflow (experiment tracking, model registry, serving), Kubeflow Pipelines, AWS SageMaker Pipelines, Azure ML (including Prompt Flow for LLM ops), Vertex AI Pipelines, or Weights & Biases (W&B) Implement CI/CD for ML: automated model training triggers, hyperparameter tuning pipelines, model evaluation gates, staging/canary deployment patterns, and champion-challenger frameworks Govern LLMOps at enterprise scale: RAG pipeline architecture and optimization (chunking strategies, embedding models, vector database selection—Pinecone, Weaviate, Qdrant, pgvector), prompt versioning, evaluation frameworks (RAGAS, LangSmith, Phoenix), and inference cost governance Implement production AI monitoring: model drift detection, data distribution shift alerting, prediction quality monitoring, and responsible AI dashboards (fairness metrics, explainability outputs, bias detection) 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 Cloud & Engineering Infrastructure Architect 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 Govern 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 Product & Engineering Economics Build engineering unit economics models for PE audiences: fully-loaded cost per feature, R&D cost as % of revenue, AI-driven capacity creation, developer FTE equivalent savings, and time-to-market acceleration Design and run AI-assisted product development programs: AI-augmented backlog refinement (LLM-based story generation and acceptance criteria drafting), AI-driven roadmap prioritization, and sprint velocity improvement using AI coding tools Lead technical due diligence: assess SDLC maturity, code quality (static analysis outputs, test coverage, cyclomatic complexity), architecture scalability, security posture, and AI tooling readiness as inputs to investment thesis and value creation plan Align engineering operating models to PE value creation: headcount optimization through AI productivity gains, offshore/nearshore engineering leverage, outsourced vs. in-house AI tooling decisions, and exit readiness preparation Qualifications 8–12+ years in software engineering leadership, technical consulting, product development, or engineering transformation—with a meaningful portion in hands-on operator or senior practitioner roles, not exclusively advisory Demonstrated track record of leading complex, multi-workstream engineering transformation programs with measurable financial outcomes—R&D cost reductions, EBITDA improvements, or engineering productivity gains delivered and realized Tangible operating experience within or directly alongside engineering organizations: has personally managed engineering teams, governed SDLC programs, led DevOps transformations, rolled out AI coding tooling at scale, or owned technical delivery accountability—not just advised on them Experience working with private equity firms or PE-backed portfolio companies, particularly software or technology-enabled businesses, with understanding of deal timelines, value creation plans, and exit preparation Proven ability to manage senior client relationships at the CTO, CPO, or CEO level and translate engineering complexity into financial impact narratives Education Undergraduate degree in computer science, software engineering, or a related technical field strongly preferred; MBA or advanced degree preferred Relevant certifications valued: AWS/Azure/GCP Developer or Solutions Architect Professional, GitHub Copilot for Business, Certified Kubernetes Administrator (CKA), HashiCorp Terraform Associate, Google Professional MLOps Engineer Preferred Background Experience at a top-tier consulting firm, software company, or PE-backed technology business with hands-on engineering delivery—or direct operator experience as VP Engineering, Head of Platform, or Engineering Director Track record of DORA metric uplift, AI coding tool rollouts at scale, SDLC modernization, or MLOps platform deployments with quantified financial outcomes Active codi