Senior Manager, Applied AI

Position Summary

The Manager, Applied AI leads TTEC’s AI Delivery Team, a focused enterprise capability for delivering, operating, and scaling responsible AI. The team converts prioritized business needs into secure, reliable, and measurable AI solutions, owning reusable platforms, technical patterns, integrations, targeted Forward Engineering support, and production operations while enabling teams across the enterprise to build within approved guardrails. This role is accountable for team leadership and execution, including capacity, prioritization, talent, and delivery management. At launch, the role also carries AI product leadership responsibilities, shaping opportunities, defining the delivery backlog, and driving adoption, until a dedicated AI Product Leader is added as the team matures. The successful candidate combines strong people leadership with enough technical depth to guide architecture, integration, and AI operations work, builds trusted partnerships with business leaders, the development-focused organization, Data Engineering, Security, Legal, Privacy, and RAIC, and keeps the team focused on measurable business outcomes over technology experimentation alone.

• AI Delivery Team leadership (AI Architect, Integration Engineers, AI Operations Engineers, and future Forward Engineers and AI Product Leader)

• Intake, prioritization, and delivery management (Shape, Design, Build or Partner, Enable, Operate, and Scale lifecycle)

• AI platforms and reusable patterns (agents, copilots, connectors, and production-readiness criteria)

• Enterprise integration and AI action enablement (APIs, MCP servers, connectors, and workflow automation)

• AI production operations (monitoring, incident response, performance management, and usage analytics)

• AI economics and consumption (token utilization, API consumption, budget forecasting, and cost efficiency)

• Responsible AI governance alignment (Security, Legal, Privacy, and RAIC standards and oversight)

• AI adoption and enablement (federated delivery by business and technology teams within approved guardrails)

Duties & Responsibilities

• Lead, coach, and develop the AI Delivery Team, including the AI Architect, Integration Engineers, and AI Operations Engineers on Day 1, and Forward Engineers and an AI Product Leader as the team matures.

• Own team capacity planning, work prioritization, and delivery management so the team’s work reflects enterprise priorities and measurable business value.

• Lead the Shape stage of the delivery lifecycle, confirming the problem, value, scope, delivery path, sponsorship, and ownership for each opportunity before work begins.

• Match each opportunity to the appropriate delivery path based on scale, complexity, and organizational impact, and transition broader enterprise and cross-functional work to the development-focused organization.

• Establish and run the team’s operating rhythm across the full lifecycle (Shape, Design, Build or Partner, Enable, Operate, and Scale), with clear handoffs and accountability at each stage.

• Stand up the Day 1 foundation: establish the technical foundation, integrate priority systems, stabilize AI operations, create initial support processes, and define the delivery backlog.

• Ensure production reliability, telemetry, cost, and support are treated as part of every solution rather than post-launch work, and oversee monitoring, incident response, and performance management for production AI services.

• Manage AI consumption and cost, including token and API utilization, budget forecasting, and cost efficiency, and report on tokenomic outcomes.

• Embed architectural, security, privacy, legal, and responsible AI requirements throughout delivery, partnering with Security, Legal, Privacy, and RAIC on standards, reviews, risk classification, and exceptions.

• Partner with Data Engineering as a shared service, defining AI data and retrieval requirements while the data organization provides pipelines, quality, access, indexing, and governance capabilities.

• Drive AI adoption and enablement by helping business and technology teams innovate safely within approved standards and guardrails, and by reusing successful patterns across the enterprise.

• Track and report team performance to leadership across delivery, business value, quality and trust, operations, economics, and federation measures.

·Lead the team’s evolution from its Day 1 structure to the long-term model, including onboarding Forward Engineers, right-sizing AI Operations, and separating people leadership from product and innovation leadership.

·Recruit, onboard, and retain talent, and build clear career paths and skill development in agent design, context engineering, integration, and AI observability.

·Keep the team and TTEC informed on the latest AI innovations, product updates, and guardrails, and translate them into roadmap and backlog priorities.

·Communicate delivery status, risks, value realized, and investment needs to executive stakeholders in clear business terms.

·Foster a team culture of accountability, collaboration, continuous improvement, and responsible AI use.

Basic Qualifications

• BA/BS in Computer Science, Information Systems, Engineering, or related field, or equivalent work experience.

• 8+ years of experience in software engineering, systems engineering, cloud, or enterprise technology delivery, including 3+ years of people leadership managing technical teams.

• Hands-on experience delivering or operating AI, machine learning, or intelligent automation solutions in an enterprise environment.

• Working knowledge of generative AI concepts, including LLMs, agents, copilots, prompt and context engineering, retrieval-augmented generation (RAG), and evaluation.

• Demonstrated experience managing delivery across a portfolio of work, including intake, prioritization, capacity planning, and backlog management.

• Experience with enterprise integration patterns, including REST APIs, connectors, identity and authentication, and workflow automation.

• Understanding of production operations practices, including monitoring, observability, incident management, performance tuning, and service reliability.

• Experience managing technology consumption and cost, including budgeting, forecasting, and platform or vendor cost optimization.

• Working knowledge of security, privacy, and compliance requirements as they apply to AI and data, including data access controls and responsible AI principles.

• Experience partnering with application development, data engineering, security, legal, and business stakeholders to deliver cross-functional outcomes.

• Experience working within enterprise incident, problem, and change management processes in an ITIL-based environment.

• Strong executive communication, stakeholder management, and change leadership skills, with the ability to translate technical work into business value.

Preferred Qualifications

·Experience with Microsoft AI and automation platforms such as Azure AI Foundry, Microsoft Copilot Studio, Microsoft 365 Copilot, Power Automate, and Azure Logic Apps.

·Familiarity with agent frameworks and protocols such as Semantic Kernel, LangGraph, AutoGen, and Model Context Protocol (MCP).

·Experience with AI observability and evaluation tools such as PromptFlow, LangSmith, or similar platforms.

·Experience integrating with enterprise platforms such as Microsoft 365, Salesforce, ServiceNow, Workday, SAP, or Jira.

·Familiarity with data platforms that support AI retrieval, such as Microsoft Fabric, Databricks, Azure Data Factory, vector databases, and knowledge indexing.

·Experience with Agile, product management, or portfolio management practices and tools such as Asana or Jira.

·Relevant certifications such as Microsoft Certified: Azure AI Engineer Associate, Azure Solutions Architect Expert, or equivalent AI or cloud certifications.

What Sets You Apart

·Experience building a new team from the ground up, establishing structure, operating rhythm, and trust in a newly formed function.

·A business-outcomes mindset that prioritizes measurable value, adoption, and process improvement over technology experimentation alone.

·Ability to set and hold clear delivery boundaries, taking on the right work and routing broader work to the right partners.

·Curiosity and fluency with the fast-moving AI landscape, balanced by sound judgment on risk, governance, and cost.

·Relationship builder who creates alignment, trust, and shared accountability with peers, cross-functional partners, and leadership.