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
What You Will Bring
10+ years of experience in Professional Services, enterprise SaaS implementation, consulting, Managed Services, customer delivery leadership, or forward-deployed enterprise technology roles
Demonstrated experience leading complex enterprise customer programs from discovery and solution design through production deployment, adoption, and measurable outcomes
Experience building or scaling Services teams and delivery models for technically complex SaaS, data, AI, or agentic products
Strong operating discipline across governance, resourcing, utilization, delivery quality, margin, and change management
Strong working knowledge of modern enterprise data platforms and architectures
Experience with Databricks strongly preferred
Experience with APIs, enterprise integrations, data ingestion, analytics workflows, and cloud environments
Hands-on working knowledge of generative AI and agentic systems, including use-case discovery, workflow and agent design, orchestration, integrations, evaluation/testing, guardrails, observability, human-in-the-loop patterns, and production readiness
Experience working directly with enterprise customers to identify high-value AI use cases and take them from pilot to production at scale
Strong understanding of the differences between deterministic software delivery and probabilistic AI systems, including the need for evaluation, monitoring, iteration, and operational guardrails
Ability to translate an enterprise business problem into an executable AI/agent solution, distinguish configuration and Services work from true product or Engineering work, and determine when specialist Engineering support is required
Strong executive communication and customer-facing leadership skills
Strong commercial judgment and understanding of Services economics
Experience working cross-functionally with Sales, Product, Engineering, Support, post-go-live customer teams, and Finance.
Experience leading distributed or global delivery teams
What Will Make You Stand Out
Deep Databricks experience
Experience in enterprise AI, agentic AI, developer platform, data-intensive SaaS, or forward-deployed technology companies
Experience deploying AI agents or LLM-enabled enterprise workflows into production, including integration, evaluation, reliability, security/governance considerations, and ongoing optimization
Experience building Managed Services or productized Professional Services offerings
Experience managing Services revenue, utilization, and margin
Experience with supply chain, procurement, manufacturing, logistics, compliance, or risk management
Experience with U.S. and India delivery models
Background with enterprise AI and modern platform companies such as Notion, ElevenLabs, OpenAI, Anthropic, Databricks, Snowflake, or similar environments, as well as high-quality consulting or enterprise software organizations with strong implementation disciplines
What's in it for you?
At Resilinc, we’re fully remote, with plenty of opportunities to connect in person. We provide a culture where ownership, purpose, technical growth and a voice in shaping impactful technology are at our core. Oh, and the perks? Full-stack benefits for health, wealth and wellbeing to keep you thriving. Check in with your talent acquisition contact for a location-specific FAQ.
Curious to know more about us? Dive in at
More great news! Resilinc is backed by Vista Equity Partners
Originally posted on Himalayas
Who can apply
Eligible countries: United States. Accepted UTC offsets: UTC-10, UTC-9, UTC-8, UTC-7, UTC-6, UTC-5, UTC+14. Review the full description for employer-specific work authorization, residency and schedule requirements.