{"id":927697,"url":"https://alion.io/job/resilinc-director-professional-services","title":"Director, Professional Services","company":{"id":14915,"name":"Resilinc","domain":"resilinc.com","url":"https://alion.io/company/resilinc","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":null},"role":"Customer Success","role_family":"Customer Success","seniority":"head","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["India"],"countries":["IN"],"hiring_countries":["IN"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":27000,"max_usd":55000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":393},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Databricks","optional":false},{"name":"ElevenLabs","optional":false},{"name":"Go","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Notion","optional":false},{"name":"OpenAI","optional":false},{"name":"Snowflake","optional":false}],"status":"live","first_seen_at":"2026-09-15T07:41:46Z","employer_posted_date":"2026-09-15","last_verified_at":"2026-11-04T00:10:12Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":49},"description":"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.\nThis 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.\nThe 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.\nThis 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.\nWhat You Will Do\nLead End-to-End Services Delivery\nOwn delivery from solution scoping through implementation, customer acceptance, and adoption-ready handoff.\nEnsure scope, technical dependencies, success criteria, timelines, and resource requirements are understood before customer commitments are finalized.\nDrive faster time-to-value, predictable deployment, and clear accountability for program completion.\nPersonally engage in the most strategic and complex customer programs and act as the senior Services executive for critical customer engagements.\nPartner with Sales, Solution Engineering, Product, Engineering, Support, and customer teams to ensure commitments are feasible and executable.\nBuild a Technically Capable Services Organization\nBuild a Services team that can independently deliver repeatable enterprise implementations and AI/agent deployments with minimal reliance on core Engineering.\nDevelop capability across:\nCustomer environment setup and provisioning\nEnterprise data ingestion and readiness\nAPI and integration workflows\nPlatform configuration\nSupply-chain mapping and validation\nRisk and event monitoring\nConfigurable analytics and customer-specific views\nAI workflow, agent design, configuration, orchestration, evaluation, and production readiness\nTroubleshooting, testing, and validation\nCustomer administrator and end-user enablement\nEstablish 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.\nDefine technical skill expectations, assess gaps, and continuously raise the capability bar across the Services organization.\nLead Enterprise AI and Agent Deployments\nOwn the Services methodology for moving enterprise AI and agent use cases from discovery and prototype into secure, reliable production deployment.\nEnsure 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.\nEstablish repeatable practices for agent evaluation, testing, guardrails, monitoring, human escalation, reliability, and continuous improvement after launch.\nPartner closely with Product and Engineering to turn patterns from strategic customer deployments into reusable capabilities, implementation assets, and product roadmap input.\nEstablish the Services + Engineering Engagement Model\nDefine clear rules for when work should be delivered independently by Services and when specialist Engineering support is required.\nIdentify complex technical dependencies during solutioning and scoping rather than after delivery issues emerge.\nEnsure strategic or customer-specific engineering requirements receive the right technical resources.\nReduce avoidable dependency on Product and Engineering for repeatable customer work.\nBuild Repeatable and Scalable Delivery Models\nCreate and continuously improve standardized approaches for:\nSolution scoping\nOnboarding and implementation\nData readiness and integrations\nAI and agent solution design, configuration, evaluation, production readiness, and enablement\nCustomer acceptance\nHypercare\nManaged Services\nChange-order management\nTransition into post-go-live adoption and consumption\nUse AI, automation, reusable assets, evaluation frameworks, playbooks, and partner capacity so customer volume can grow faster than Services headcount while improving implementation quality.\nOwn Services Economics and Capacity\nOwn Services utilization, billability, revenue, delivery margin, resource planning, and change-order discipline.\nBuild a capacity model covering implementation resources, strategic programs, specialist Engineering dependencies, U.S./India delivery, and future hiring requirements.\nPartner with Finance and Commercial leadership to ensure customer-specific work is appropriately scoped, priced, and delivered.\nIdentify opportunities to convert repeatable customer needs into productized or recurring Services offerings.\nDevelop and Scale Managed Services\nBuild recurring Managed Services offerings for customers that require ongoing support beyond initial implementation.\nThese may include:\nData and supply-chain validation\nOngoing analytics and reporting\nTechnical enablement\nCompliance-related support\nAI and agent workflow optimization, evaluation, monitoring, and continuous improvement\nCustomer-specific operating services\nOngoing platform administration and adoption support\nPartner across Customer, Product, Engineering, and Commercial teams to ensure these offerings deliver measurable customer value and sustainable Services economics.\nDrive Adoption-Ready Handoffs\nEnsure every implementation transitions with clear:\nGo-live and completion dates\nAdoption and consumption objectives\nUser, AI-workflow, and agent usage expectations\nCustomer success criteria\nRemaining technical barriers\nOwnership for post-go-live outcomes\nProfessional Services owns successful deployment and readiness for adoption. Ongoing consumption, value realization, retention, and expansion transition to the post-go-live customer organization.\nWhat Success Looks Like\nSuccess in this role will be measured by:\nFaster customer time-to-value\nHigher on-time implementation and acceptance rates\nImproved implementation quality and predictability\nIncreased Services self-sufficiency\nReduced avoidable Engineering dependency\nHigher utilization and billability\nImproved Services revenue and margin contribution\nStronger scope and change-order discipline\nIncreased repeatability, automation, and reuse of proven AI/agent deployment patterns\nImproved capacity planning\nGrowth in Managed Services\nStronger customer adoption readiness at handoff\nImproved customer satisfaction with implementation and Services\nHigher percentage of AI/agent use cases reaching production and delivering agreed business outcomes\nImproved agent quality, reliability, and production readiness across deployed customer workflows\nWhat You Will Bring\n10+ years of experience in Professional Services, enterprise SaaS implementation, consulting, Managed Services, customer delivery leadership, or forward-deployed enterprise technology roles\nDemonstrated experience leading complex enterprise customer programs from discovery and solution design through production deployment, adoption, and measurable outcomes\nExperience building or scaling Services teams and delivery models for technically complex SaaS, data, AI, or agentic products\nStrong operating discipline across governance, resourcing, utilization, delivery quality, margin, and change management\nStrong working knowledge of modern enterprise data platforms and architectures\nExperience with Databricks strongly preferred\nExperience with APIs, enterprise integrations, data ingestion, analytics workflows, and cloud environments\nHands-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\nExperience working directly with enterprise customers to identify high-value AI use cases and take them from pilot to production at scale\nStrong understanding of the differences between deterministic software delivery and probabilistic AI systems, including the need for evaluation, monitoring, iteration, and operational guardrails\nAbility 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\nStrong executive communication and customer-facing leadership skills\nStrong commercial judgment and understanding of Services economics\nExperience working cross-functionally with Sales, Product, Engineering, Support, post-go-live customer teams, and Finance.\nExperience leading distributed or global delivery teams\nWhat Will Make You Stand Out\nDeep Databricks experience\nExperience in enterprise AI, agentic AI, developer platform, data-intensive SaaS, or forward-deployed technology companies\nExperience deploying AI agents or LLM-enabled enterprise workflows into production, including integration, evaluation, reliability, security/governance considerations, and ongoing optimization\nExperience building Managed Services or productized Professional Services offerings\nExperience managing Services revenue, utilization, and margin\nExperience with supply chain, procurement, manufacturing, logistics, compliance, or risk management\nExperience with U.S. and India delivery models\nBackground 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 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