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Accenture

Advises businesses and runs their technology

AI Platform Engineering Senior Manager

Engineering LeadershipPosted on Nov 13
Full-timeHybrid

About the Job

Requirements

  • At least 10 years of experience in technology, data, or AI roles across consulting or industry, with a track record across varied industries and business problems.
  • At least 5 years leading technology, data, or AI programs end to end, including managing complexity, ambiguity, shifting scope, and senior stakeholders.
  • At least 5 years directly managing and developing technical practitioners, with demonstrated ability to grow them into workstream and program leaders.
  • At least 5 years leading transformation in customer service, contact center, or customer experience operations, with a working understanding of containment, first-contact resolution, average handle time, customer satisfaction, and cost-to-serve metrics and how AI affects them.
  • Experience leading Forward Deployed Engineers (FDEs) or similar client-embedded technical teams, such as solutions architects, sales engineers, or technical consultants.
  • Deep experience designing and operating enterprise-scale cloud infrastructure on AWS, Azure, or Google Cloud, including containers and orchestration with Kubernetes, infrastructure as code, and continuous integration and continuous delivery.
  • Experience running AI and machine learning 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, or NICE CXone, including voice and telephony infrastructure such as Session Initiation Protocol and WebRTC, real-time speech-to-text and text-to-speech services, and peak-volume capacity planning.
  • Experience building service data and analytics foundations, including interaction data such as calls, chats, and transcripts, workforce and quality data, and reporting and dashboards used by service leaders.
  • A track record of leading business development by shaping proposals, scoping programs, and presenting to clients in a way that builds confidence and closes deals.
  • Ability to hold difficult conversations with senior client stakeholders, guide them to outcomes that strengthen relationships, and set expectations that protect delivery teams.
  • Ability to connect technical decisions to business outcomes and influence cross-functional decision making.

Responsibilities

  • 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 by challenging team designs, unblocking delivery, and giving clients a candid view of what it takes to get AI into production.
  • Direct how FDEs embed with clients: run discovery to find high-value use cases, prototype quickly to prove value, and own solutions through production rollout, adoption, runbooks, and handoff to client or managed-delivery teams.
  • Own the design and governance of service AI platform infrastructure, including model serving and inference, deployment pipelines, environments, and tooling across cloud and hybrid estates.
  • Provide FDEs a platform path from prototype to production using environments, platform-native tooling, continuous integration and continuous delivery, and observability so that hardening becomes a standard step rather than a rebuild.
  • Lead performance and capacity engineering for real-time service workloads, including latency budgets for voice and chat, concurrency planning for seasonal and event-driven peaks, load testing, and scaling strategies.
  • Manage AI cost at scale through inference and token economics, model routing, caching, right-sizing, and FinOps reporting.
  • Shape data foundations behind service intelligence, including service data lakes, pipelines, and platforms powering reporting, dashboards, and analytics agents.
  • Establish AI platform operations with service-level objectives, observability, and incident management, and ensure models hand over cleanly to client teams.
  • Set performance targets around customer-perceived measures, such as the pause before a voice agent responds, and give design and engineering teams clear constraints.
  • Grow a pipeline of work by building client relationships and connecting Song's service AI capabilities to client priorities; shape platform modernization and scale-up proposals to win work and generate future demand.
  • Use FDE-led discovery and rapid prototypes to prove value before full engagement, and partner with sales teams on business cases, return-on-investment models, executive narratives, and requests for proposals.
  • Develop thought leadership on operating AI economically and reliably at scale in customer service to open conversations with client technology leaders.
  • Strengthen relationships with cloud, data, and AI platform partners to improve joint solutions and go-to-market efforts.
  • Lead, coach, and grow FDEs, platform engineers, and architects, developing their technical depth, leadership skills, and client acumen so they can become workstream and program owners.
  • Build inclusive, high-performing teams that support and challenge practitioners and help them grow as leaders.
  • Scale the FDE capability within Service AI & Data by hiring suitable hybrid profiles, defining how FDEs engage with clients, and establishing career paths from FDE to workstream and program leadership.
  • Apply AI tooling across story grooming, solutioning, testing and triage, and development to improve delivery speed and quality.
  • Codify platform blueprints, performance benchmarks, and operating playbooks to make delivery repeatable across clients.

Desired Qualifications

  • A hands-on FDE or engineering background.

About the Company

Accenture

Advises businesses and runs their technology

Accenture advises large businesses and public agencies on strategy, builds their technology systems and runs ongoing operations. Its teams connect business planning with implementation, covering areas such as cloud migration, cybersecurity and customer experience. Consultants work with clients to redesign processes, while engineers integrate software from technology partners and maintain the resulting systems for multinational organizations. Clients pay for project work or continuing services, including application support and business processes such as finance and customer service. Accenture was founded in 1989 and has its headquarters in Dublin, Ireland.

Company size
10,001+
Company stage
IPO
Headquarters
Dublin, Ireland
Founded
1989

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