Director, Professional Services

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