Director, Professional Services

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.

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

• 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

• 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