Agiloft

Platform Engineer

• Build and maintain software applications in our suite of enterprise software products
• Work with product managers and designers to brainstorm bottom-up solutions to novel problems
• Collaborate with AI engineers to efficiently utilize commercial large language model (LLM) APIs
• Continuously improve the code quality, extensibility, scalability, and test coverage of software projects
• Make architecture and cloud-engineering decisions in conjunction with other engineers
• Develop and improve infrastructure-as-code stacks, CI/CD pipelines, and monitoring/observability tools
• Improve internal developer tools, workflows, and best practices to enhance team efficiency
• Provide consistent feedback to management regarding the quality of the team, the state of the tech stack, and relationships with stakeholders
• Troubleshoot and resolve complex issues across the entire application stack
• Mentor junior team members
• Participate in incident response and disaster recovery efforts
• Leverage AI-assisted tools to enhance productivity, streamline workflows, and support day-to-day activities
• Other duties as assigned

Senior Platform Engineer

Solutions Engineer

• Lead the design and architecture of customer’s Agiloft instance, ensuring solutions are clean, effective, scalable, maintainable, and meet the complexity of enterprise-level needs.
• Capture, analyze, and document client requirements.
• Develop comprehensive design documents and implementation plans for team collaboration and execution.
• Provide mentorship and constructive feedback to junior team members, ensuring high-quality deliverables.
• Manage client interactions, addressing escalations with a solutions-focused mindset.
• Deliver thorough documentation and client training to ensure successful solution adoption.
• Offer feedback to product and engineering teams to drive continuous product improvement.
• Support and advise system integration (SI) partners to enhance overall implementation outcomes.
• Contribute to internal initiatives aimed at improving implementation processes and client success.
• Maintain current, deep knowledge of Agiloft product offerings, roadmap, and best practices.
• Apply product knowledge effectively in delivering high-quality consulting services
• Proactively learn and adopt new features, including AI-enabled capabilities, and translate them into customer value.
• Actively and responsibly use AI to improve efficiency, productivity, and value delivery, continuously evaluating where AI can enhance processes, workflows, and outcomes.
• Approximately 10% travel required.
• Other duties as assigned

Senior Solution Architect

Backend Engineer

• Lead the design and development of core platform features using Python and an API-first approach
• Build scalable, reliable backend services that power our applications, integrating seamlessly with a modern cloud-based architecture
• Collaborate with design and product teams to ensure that what we’re building is scalable, maintainable, and secure
• Contribute to infrastructure and deployment workflows in our AWS serverless environment (e.g., Lambda, API Gateway, DynamoDB)
• Independently lead proof-of-concept projects and tackle complex architectural and engineering challenges
• Leverage AI-assisted tools to enhance productivity, streamline workflows, and support day-to-day activities

Staff Software Engineer – Backend

Data Engineer

• Own the end-to-end data architecture for the Data Warehouse Foundation, designing for AI-first consumption across GPT assistants, AI agents, predictive models, and operational intelligence — in addition to BI and reporting.
• Lead data modeling across all 11 departments, designing canonical enterprise data models that serve cross-functional AI and analytics use cases without duplication or fragmentation.
• Design and implement the contextual intelligence layer — including RAG architecture, vector store strategy, knowledge base ingestion pipelines, and document and unstructured data processing — that powers Agiloft's enterprise knowledge system.
• Build and maintain the agentic data integration layer: real-time and near-real-time data access patterns, agent memory and state persistence design, orchestration data requirements, and agent output integration back into the warehouse.
• Own the AI/ML feature layer — feature engineering strategy and standards, training data pipeline design, feature store architecture, and model output integration — enabling predictive analytics across churn, pipeline health, and operational forecasting.
• Design and govern the operational data and GPT context layer, including structured context feed design for GPT assistants, data freshness and access SLAs for AI use cases, and cross-departmental data reuse standards.
• Lead the Data Warehouse Foundation build in partnership with the external consulting team — setting architecture standards, reviewing implementation against AI-first principles, and ensuring the five-wave build plan delivers a foundation that serves the full intelligence architecture.
• Design and manage data ingestion, ELT/ETL, and orchestration pipelines across all source systems, ensuring reliability, performance, and cost efficiency.
• Establish and enforce AI data engineering standards across the organization — prompt-adjacent data design, agent data access patterns, reusable pipeline components, and quality assurance processes.
• Own data access policy design and least-privilege access controls in partnership with Security, ensuring data made available to AI systems is governed, auditable, and compliant.
• Define data quality standards and monitoring processes for AI-consumed data, where quality failures have direct impact on model and agent performance.
• Partner with the Principal Data and Integrations Architect on infrastructure design, ensuring data modeling and AI consumption requirements are incorporated into pipeline and architecture decisions from the start — not retrofitted after build.
• Partner with the VP FP&A and Manager of BI & Data to ensure the semantic and metrics layers are technically sound and serve both AI use cases and reporting requirements.
• Manage the AI Ops data architecture roadmap, translating business and AI use case requirements from all 11 departments into sequenced, prioritized technical work.
• Maintain documentation and knowledge transfer standards for all data architecture, pipelines, and integration patterns — ensuring AI Ops-built infrastructure is reusable, auditable, and not dependent on any single individual.
• Collaborate with the AI Agent Engineer and GPT & AI Systems Lead to ensure data infrastructure supports agent orchestration, retrieval-augmented generation, and multi-step reasoning workflows.
• Define the roadmap for data science and AI data work in partnership with the VP of AI Operations — this role does not take direction from IT on resource allocation or prioritization. All roadmapping is managed within AI Operations.
• Evaluate and recommend data tooling, frameworks, and platform components in alignment with AI Ops' technology-agnostic, build-for-leverage approach.
• Other duties as assigned.

AI Data Platform Lead

Business Intelligence Analyst

• Define business data requirements and dimensional models for reporting and analytics use cases.
• Partner with AI Operations on warehouse architecture decisions to ensure reporting needs are supported.
• Validate data transformations for accuracy and metric consistency.
• Partner with the Integrations Engineer to prioritize and oversee data ingestion, transformation, and pipeline development for executive reporting, board reporting, and operational dashboards.
• Handle escalation for data quality, discrepancies, and metric disputes.
• Own change management for reporting data models, KPI definitions, and reporting logic, including versioning and communication.
• Establish and maintain a centralized KPI registry, including standardized metric definitions across Finance, Sales, Operations, and Product.
• Chair and manage the cross-functional Data Committee to ensure enterprise-wide governance and alignment.
• Develop KPI governance policies and reporting standards, including data ownership, access controls, documentation standards, and quality SLAs.
• Design and standardize executive dashboards and reporting packages, reducing shadow reporting and spreadsheet fragmentation.
• Support Finance with forecasting models, ARR reporting, SaaS metrics, and board-ready reporting packages.
• Evaluate and optimize BI tools and reporting platforms to improve scalability and self-service analytics.
• Responsible for KPI and metric definitions within the semantic layers.
• Implement data quality monitoring processes and ensure integrity of critical business datasets.
• Collaborate with AI Operations to ensure reporting models and KPI frameworks align with AI-driven use cases and operational automation initiatives.
• Drive process improvements related to reporting automation, metric clarity, and cross-functional data workflows.
• Partner with GTM Ops, RevOps, and Product Ops to ensure operational reporting aligns with financial reporting standards.
• Provide analytical support for strategic initiatives, pricing analysis, business cases, and performance improvement opportunities.
• Other duties as assigned.

Manager, Data Operations – Business Intelligence

Technical Customer Success

• Lead structured Integration Success Reviews across implementation and post-go-live milestones
• Evaluate integration architecture for scalability, reliability, and observability
• Translate business requirements into stable, supportable integration designs
• Identify technical risks early and recommend actionable improvements
• Provide hands-on problem solving in live customer environments
• Analyze task consumption patterns and identify inefficiencies or instability drivers
• Recommend optimized integration patterns (event-based, delta-based, etc.)
• Support high-risk accounts with overconsumption or unstable integrations
• Develop prioritized remediation and stabilization plans
• Improve overall system efficiency and cost alignment
• Partner with Customer Success to activate underutilized Integration Hub accounts
• Remove technical blockers to enable production adoption
• Guide customers from low usage to stable, value-driven integration deployment
• Support retention and expansion through improved customer outcomes
• Build and refine review frameworks, templates, and integration playbooks
• Standardize repeatable practices for scalable customer success
• Partner with Product to identify gaps and influence roadmap priorities
• Collaborate across Customer Success, Professional Services, and partners
• Define engagement models based on risk and complexity levels

Technical Customer Success Manager – Integrations

Product Marketing

• Build and maintain a continuous, high-quality intelligence loop leveraging AI, drawing deep insights from customers, prospects, analysts, partners and the competitive landscape.
• Convert signals into clear, prioritized guidance that helps to shape the product roadmap.
• Define how we win and sharpen our market narrative, assets and tools to it.
• Power the commercial success of our GTM teams and influence our business strategies.
• Leverage AI beyond the intelligence loop to operationalize the consumption, cadence and impact of the intelligence across the business.
• Act as a leading voice in the external market through events, social, and webinars articulating win narratives and thought leadership positioning.
• Direct management responsibilities for product marketing, analyst relations, public relations, community, customer expansion and advocacy.

Senior Director of Product Marketing

Consultant

• Serve as a subject matter expert in the Agiloft platform and the broader CLM market
• Partner closely with Account Executives to develop and execute sales strategies
• Act as a trusted advisor to customers by aligning platform capabilities to business outcomes
• Lead discovery, solution design, and value articulation across complex enterprise sales cycles
• Build and deliver tailored product demonstrations aligned to key business drivers
• Manage and lead Proof of Concept (POC) engagements with clear success criteria
• Support the creation of high-quality proposals, RFP/RFI responses, and customer-facing deliverables
• Apply knowledge of machine learning (ML), natural language processing (NLP), and AI-driven workflows to position differentiated solutions and educate customers
• Collaborate with Product and Engineering teams to provide structured customer feedback

Senior Platform Engineer

Senior Solution Architect

Senior Solution Architect

Staff Software Engineer – Backend

AI Data Platform Lead

Manager, Data Operations – Business Intelligence

Technical Customer Success Manager – Integrations

Senior Director of Product Marketing

Principal Solution Consultant