Director / Senior 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