Director / Senior Director, Professional Services (AI & Agentic Platforms) - Resilinc

Core Responsibilities

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.

Key Accountabilities

The right candidate must combine:

• 10+ years of experience in Professional Services, enterprise SaaS implementation, consulting, or Managed Services
• Enterprise SaaS delivery experience with executive customer credibility and technical fluency across data, APIs, integrations, cloud platforms, AI workflows, and agents
• Hands-on fluency in scoping, configuring, integrating, testing, governing, deploying, and improving enterprise AI and agents in production

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
• Engage in the most strategic and complex customer programs as the senior Services executive
• Partner with Sales, Solution Engineering, Product, Engineering, Support, and customer teams to ensure commitments are feasible and executable

Build a Technically Capable Services Organization

• Develop capability across 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
• 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

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
• Reduce avoidable dependency on Product and Engineering for repeatable customer work

Build Repeatable and Scalable Delivery Models

• Create standardized approaches for solution scoping, onboarding, implementation, data readiness, AI and agent solution design, evaluation, production readiness, enablement, customer acceptance, hypercare, Managed Services, and change-order management
• Use AI, automation, reusable assets, evaluation frameworks, playbooks, and partner capacity to scale implementation quality while improving efficiency

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
• 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 requiring ongoing support, including data validation, analytics, technical enablement, compliance support, AI/agent workflow optimization, and platform administration

Drive Adoption-Ready Handoffs

• Ensure every implementation transitions with clear go-live dates, adoption objectives, user expectations, success criteria, and ownership for post-go-live outcomes

What Success Looks Like

• 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, 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, 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