AI Platform Engineering Senior Manager

Job description

Who we are: Accenture Song Service helps leading brands turn customer interactions into an engine for growth, moving service beyond a cost center to something that builds trust, loyalty, and share of wallet. Our Service practice works end to end: we shape the vision and strategy for the service function and help clients implement and run it, innovate the products and services customers rely on, equip client customer service and customer experience teams with the right technology and talent, and turn customer data into insights that strengthen client’s relationship with their customers. Service AI & Data puts AI at the heart of this work. We help clients reinvent how they serve their customers and support their people: conversational AI and AI agents that resolve customer needs across voice, chat, and messaging; copilots that help service agents and field teams do their best work; and the data foundations, data lakes, reporting, and analytics agents that turn every interaction into insight. The strategies we shape and the experiences we design are delivered into production at enterprise scale. Who you are: An AI Infrastructure Architecture leader who is ready to make sure our clients have the foundation to run AI in customer service at scale. You lead teams of platform engineers and Forward Deployed Engineers (FDEs) who design, implement, and govern the compute, deployment infrastructure, and tooling AI depends on, and you tune how those workloads run, so a virtual agent answers in real time on the busiest day of the year and the cost per interaction still makes business sense. You are a confident client partner and team leader with deep experience managing senior stakeholder relationships. You will need to inspire and challenge a design, unblock a team, and tell a client the hard truth about what it takes to get AI into production. You develop practitioners into leaders and form clear points of view that help clients navigate how AI is reshaping customer service. Your teams include FDEs, hybrid practitioners who embed directly with clients to discover, prototype, and deploy AI solutions into production. You may have been one yourself; now your job is to lead them, set the standard for how they work, and scale their impact across engagements. The work: In this role you will have accountability across three dimensions: Client Delivery: Oversee AI platform programs, providing technical direction and quality oversight across leads and their teams while maintaining accountability for delivery health, team performance, and measurable outcomes such as availability, latency, throughput, and cost per interaction. Stay connected to the work: inspire and challenge your teams' designs, unblock delivery when it stalls, and give clients a candid view of what it takes to get AI into production. Direct how your FDEs embed with clients: running discovery to find the highest-value use cases, prototyping fast to prove value, and owning solutions end to end through production rollout, adoption, runbooks, and a clean handoff to client or managed-delivery teams. Own the design and governance of AI platform infrastructure for service, including model serving and inference, deployment pipelines, environments, and tooling across cloud and hybrid estates. Give your FDEs a platform path from prototype to production, with environments, platform-native tooling, CI/CD, and observability that make hardening a solution a standard step rather than a rebuild. Lead performance and capacity engineering for real-time service workloads: latency budgets for voice and chat, concurrency planning for seasonal and event-driven peaks, load testing, and scaling strategies. Bring cost discipline to AI at scale through inference and token economics, model routing, caching, and right-sizing, with FinOps reporting that leaders can act on. Shape the data foundations behind service intelligence, including service data lakes, pipelines, and the platforms that power reporting, dashboards, and analytics agents. Stand up AI platform operations with clear SLOs, observability, and incident management, and run models that hand over cleanly to client teams. Set performance targets around what customers actually feel, like the pause before a voice agent responds, and give design and engineering teams clear constraints to design and build against. Business Development: Grow a pipeline of work by building strong client relationships and proactively connecting Song's service AI capabilities to client priorities, shaping platform modernization and scale-up proposals that win work and fuel future demand. Use FDE-led discovery and rapid prototypes to prove value before full engagement, and partner with sales teams on business cases, ROI models, executive narratives, and RFP responses. Develop thought leadership on running AI economically and reliably at scale in customer service, opening doors to new conversations with client technology leaders. Strengthen relationships with cloud, data, and AI platform partners to sharpen joint solutions and go-to-market. People & Practice Development: Lead, coach, and grow Forward Deployed Engineers and platform engineers and architects, helping them navigate challenges and grow their technical depth, leadership skills, and client acumen so they can step up as workstream and program owners. Build inclusive, high-performing teams where practitioners are supported, challenged, and able to grow into stronger leaders over time. Scale the FDE capability within Service AI & Data: hire the right hybrid profiles, define how FDEs engage with clients, and build career paths from FDE to workstream and program leadership. Bring AI-augmented delivery into your teams, applying AI tooling across story grooming, solutioning, testing and triage, and development to raise speed and quality. Codify platform blueprints, performance benchmarks, and operating playbooks that make delivery repeatable across clients. Here's what you'll need: 10+ years of experience in technology, data, or AI roles across consulting or industry, with a track record across varied industries and business problems. 5+ years leading technology, data, or AI programs end-to-end, with experience managing complexity, ambiguity, shifting scope, and senior stakeholders. 5+ years directly managing and developing technical practitioners, with demonstrated ability to grow them into workstream and program leaders. 5+ years of experience leading transformation in customer service, contact center, or customer experience operations, with a working understanding of service metrics such as containment, first-contact resolution, average handle time, CSAT, and cost-to-serve, and how AI moves them. Experience leading FDEs or similar client-embedded technical teams, such as solutions architects, sales engineers, or technical consultants; a hands-on FDE or engineering background yourself is a strong plus. Deep experience designing and operating cloud infrastructure at enterprise scale on AWS, Azure, or Google Cloud, including containers and orchestration (Kubernetes), infrastructure as code, and CI/CD. Experience running AI and ML workloads in production, including model serving, inference optimization, accelerator capacity, and managed AI platforms such as Amazon Bedrock, Microsoft Azure AI Foundry, Google Vertex AI or Gemini Enterprise Agent Platform, or Databricks Mosaic AI. A track record in performance engineering, observability, and site reliability for latency-sensitive, high-volume systems. Experience with modern data platforms such as Databricks, Snowflake, BigQuery, or Microsoft Fabric. Experience supporting high-volume contact center operations on platforms such as Amazon Connect, Genesys Cloud, NICE CXone, etc., including voice and telephony infrastructure (SIP, WebRTC), real-time speech services (speech-to-text and text-to-speech), and peak-volume capacity planning. Experience building service data and analytics foundations