AI Solution Engineering Senior Manager - CG&S

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 Solution Engineering leader who is ready to lead teams that build reliable, production AI products for customer service. You lead engineering teams, including Forward Deployed Engineers (FDEs), who integrate foundation models, RAG pipelines, agents, data services, and application components into products that customers and service employees use every day. 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 will be embed directly with clients to discover, prototype, and deploy AI solutions into production. You set the standard for how your team work, and scale their impact across engagements. The work: In this role you will have accountability across three dimensions: Client Delivery: Oversee AI product engineering 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 resolution, average handle time, customer satisfaction, and adoption. 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. Lead delivery of production AI applications for service: conversational AI agents across voice and digital channels, human agent-assist copilots, knowledge assistants, interaction summarization and quality automation, and analytics agents that let leaders ask questions of their service data in plain language. Hold your FDEs to production-grade standards: tested Python and TypeScript services, eval suites that catch regressions, observability, and clear cost, latency, and safety tradeoffs, so solutions run reliably after handoff. Set engineering standards for how AI components work together, including RAG design, prompt and context engineering, evals and guardrails, model selection, and when fine-tuning is worth the investment. Originate use cases with clients, prioritizing by measurable value and shaping engagements that ladder to a real product, not a proof of concept. Turn conversation designs, customer experience journeys, and prototypes into production code, co-creating from early concepts and feeding back what data and model behavior reveal so the experience keeps improving. Ensure what ships is secure, monitored, and responsible, partnering with specialists in MLOps, AI security, and responsible AI. Business Development: Grow a pipeline of work by building strong client relationships, proactively connecting Song's service AI capabilities to client priorities, and crafting proposals that win work our teams are proud to ship. 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 the service AI products that matter most to clients, such as agentic customer service and analytics agents, advancing Song Service as a credible and compelling voice in the industry. Strengthen relationships with AI and service platform partners to sharpen joint solutions and go-to-market. People & Practice Development: Lead, coach, and grow FDEs and the engineers around them, 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: 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 use case discovery & prioritization, story grooming, solutioning, testing and triage, and development to raise speed and quality. Codify delivery patterns, eval suites, and reusable components, and feed field learnings back into our offerings to make delivery repeatable. 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. Experience should include:A hands-on background moving AI products into production (RAG, agentic workflows, evals and guardrails). Working fluency with the FDE technical stack, including LLM APIs across frontier and open-weight models (Claude, Gemini, GPT, Llama, Mistral), agentic frameworks and MCP, RAG pipelines and vector databases, Python and TypeScript, REST and GraphQL integration, and model and prompt evals, so you can inspire and challenge your teams' technical designs. Delivery experience on at least one enterprise agent platform in production, such as Amazon Bedrock, Microsoft Azure, Google Gemini, or Databricks Mosaic. 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. Bonus points if you have: A product-led mindset that connects AI and data work to product strategy and the design-to-product pipeline. Experience leading delivery of conversational AI in production, such as virtual agents, voice bots, agent-assist copilots, and knowledge assistants, spanning conversation design, dialog and intent management, and LLM-based agents across voice and digital channels. Experience embedding AI into contact center and CRM platforms such as Salesforce Service Cloud, ServiceNow, Microsoft Dynamics 365, Genesys Cloud, NICE CXone, or Amazon Connect, and into the workflows service agents use every day. Experience with conversation intelligence, such as speech and interaction analytics, sentiment, and automated quality assurance, turning service conversations into insight at the enterprise level. A track re