Director / Senior Director, Professional Services

Job Description

Join the Future of Supply Chain Intelligence — Powered by Agentic AI

At Resilinc, we’re pioneering intelligent, autonomous systems that redefine supply chain risk management. Our agentic AI helps global enterprises predict disruptions, assess impact, and act in real time — before operations are affected. Named a 2025 Gartner® Magic Quadrant™ Leader, we’re trusted by top companies in life sciences & pharma, aerospace & defense, high tech, and automotive to protect what matters most. Be part of a team that's redefining resilience on a global scale.

But the real power behind Resilinc? Our people. We’re a fully remote, mission-led team making sure life-saving products and critical goods get where they’re needed, fast. We offer the chance to do meaningful work in a collaborative, empowering culture—where you can be an agent of change. Join us to tackle critical global challenges through high-impact work that matters.

Check out this blog to learn more about how we are impacting the world's most critical supply chains. Global Supply Chain Risks 2026: Act Faster | TEC

Resilinc | Innovation with Purpose. Intelligence with Impact.

Resilinc is hiring a Director / Senior Director, Professional Services to lead and scale a technically capable, commercially disciplined customer delivery organization for an enterprise AI and agentic platform.

This leader will own customer delivery from solution scoping through implementation, production deployment, acceptance, and transition into ongoing adoption and consumption. They will be accountable for enterprise implementations, AI and agent deployments, technical delivery, Managed Services, customer outcomes, Services economics, capacity planning, and repeatable execution across strategic customers.

The right candidate brings strong Professional Services leadership and meaningful experience taking AI-enabled or agentic enterprise solutions from customer problem definition through production deployment and measurable business value. They must combine enterprise SaaS delivery experience, executive customer credibility, and technical fluency across data, APIs, integrations, cloud platforms, AI workflows, and agents.

This is not a pure technical architect role, a generic project management role, or a traditional SaaS Services leader who is only AI-aware. It is a Services leadership role requiring hands-on fluency in how enterprise AI and agents are scoped, configured, integrated, tested, governed, deployed, and improved in production, together with commercial judgment and operating discipline.

What You Will Do

Lead End-to-End Services Delivery

• Own delivery from solution scoping through implementation, customer acceptance, and adoption-ready handoff.
• Ensure scope, technical dependencies, success criteria, timelines, and resource requirements are understood before customer commitments are finalized.
• Drive faster time-to-value, predictable deployment, and clear accountability for program completion.
• Personally engage in the most strategic and complex customer programs and act as the senior Services executive for critical customer engagements.
• Partner with Sales, Solution Engineering, Product, Engineering, Support, and customer teams to ensure commitments are feasible and executable.

Build a Technically Capable Services Organization

Build a Services team that can independently deliver repeatable enterprise implementations and AI/agent deployments with minimal reliance on core Engineering.

Develop capability across:

• Customer environment setup and provisioning
• Enterprise data ingestion and readiness
• API and integration workflows
• Platform configuration
• Supply-chain mapping and validation
• Risk and event monitoring
• Configurable analytics and customer-specific views
• AI workflow, agent design, configuration, orchestration, evaluation, and production readiness
• Troubleshooting, testing, and validation
• Customer administrator and end-user enablement

Establish strong competency across the team in Databricks, modern data platforms, APIs, cloud integrations, analytics workflows, LLM-enabled applications, agentic workflows, evaluation and testing, observability, and enterprise AI deployment patterns.

Define technical skill expectations, assess gaps, and continuously raise the capability bar across the Services organization.

Lead Enterprise AI and Agent Deployments

• Own the Services methodology for moving enterprise AI and agent use cases from discovery and prototype into secure, reliable production deployment.
• Ensure teams can define business outcomes, map existing workflows, identify the right human/agent boundaries, configure and integrate agents, establish evaluation criteria, and validate production readiness.
• Establish repeatable practices for agent evaluation, testing, guardrails, monitoring, human escalation, reliability, and continuous improvement after launch.
• Partner closely with Product and Engineering to turn patterns from strategic customer deployments into reusable capabilities, implementation assets, and product roadmap input.

Establish the Services + Engineering Engagement Model

• Define clear rules for when work should be delivered independently by Services and when specialist Engineering support is required.
• Identify complex technical dependencies during solutioning and scoping rather than after delivery issues emerge.
• Ensure strategic or customer-specific engineering requirements receive the right technical resources.
• Reduce avoidable dependency on Product and Engineering for repeatable customer work.

Build Repeatable and Scalable Delivery Models

Create and continuously improve standardized approaches for:

• Solution scoping
• Onboarding and implementation
• Data readiness and integrations
• AI and agent solution design, configuration, evaluation, production readiness, and enablement
• Customer acceptance
• Hypercare
• Managed Services
• Change-order management
• Transition into post-go-live adoption and consumption

Use AI, automation, reusable assets, evaluation frameworks, playbooks, and partner capacity so customer volume can grow faster than Services headcount while improving implementation quality.

Own Services Economics and Capacity

• Own Services utilization, billability, revenue, delivery margin, resource planning, and change-order discipline.
• Build a capacity model covering implementation resources, strategic programs, specialist Engineering dependencies, U.S./India delivery, and future hiring requirements.
• Partner with Finance and Commercial leadership to ensure customer-specific work is appropriately scoped, priced, and delivered.
• Identify opportunities to convert repeatable customer needs into productized or recurring Services offerings.

Develop and Scale Managed Services

Build recurring Managed Services offerings for customers that require ongoing support beyond initial implementation.

These may include:

• Data and supply-chain validation
• Ongoing analytics and reporting
• Technical enablement
• Compliance-related support
• AI and agent workflow optimization, evaluation, monitoring, and continuous improvement
• Customer-specific operating services
• Ongoing platform administration and adoption support

Partner across Customer, Product, Engineering, and Commercial teams to ensure these offerings deliver measurable customer value and sustainable Services economics.

Drive Adoption-Ready Handoffs

Ensure every implementation transitions with clear:

• Go-live and completion dates
• Adoption and consumption objectives
• User, AI-workflow, and agent usage expectations
• Customer success criteria
• Remaining technical barriers
• Ownership for post-go-live outcomes

Professional Services owns successful deployment and readiness for adoption. Ongoing consumption, value realization, retention, and expansion transition to the post-go-live customer organization.

What Success Looks Like

Success in this role will be measured by:

• Faster customer time-to-value
• Higher on-time implementation and acceptance rates
• Improved implementation quality and predictability
• Increased Services self-sufficiency
• Reduced avoidable Engineering dependency
• Higher utilization and billability
• Improved Services revenue and margin contribution
• Stronger scope and change-order discipline
• Increased repeatability, automation, and reuse of proven AI/agent deployment patterns
• Improved capacity planning
• Growth in Managed Services
• Stronger customer adoption readiness at handoff
• Improved customer satisfaction with implementation and Services
• Higher percentage of AI/agent use cases reaching production and delivering agreed business outcomes
• Improved agent quality, reliability, and production readiness across deployed customer workflows