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Build and deploy production AI and machine learning capabilities across Anaplan’s platform. Responsibilities include developing GenAI features, integrating and monitoring LLMs, implementing RAG and agentic systems, designing vector and knowledge graph retrieval layers, engineering batch and streaming data pipelines, and creating evaluation frameworks. The role combines advanced data engineering, MLOps, LLMOps, model serving, prompt engineering, and enterprise planning analytics.

Manage a portfolio of enterprise customers, driving Anaplan deployment, adoption, retention, renewals, expansion, and measurable business value. Act as a strategic advisor, lead executive success reviews, identify risks, develop change-management and enablement strategies, and advocate for customer needs. Collaborate with partners, professional services, renewal managers, and account executives while contributing to customer success playbooks and case studies.

Leads technical presales for enterprise customers by developing solution strategies, conducting discovery, delivering tailored demos, running proof-of-concept workshops, and shaping multi-year technology roadmaps. Partners with sales, product, customer success, specialists, and implementation partners to position AI-driven planning solutions. Applies expertise in enterprise planning, financial transformation, supply chain, workforce planning, data modeling, machine learning, and value-based selling.

Develop automated QA strategies and evaluation frameworks for GenAI and agentic systems. Build Python-based tests across APIs, UIs, performance, prompts, and model behavior. Maintain regression suites, test datasets, and evaluation metrics covering accuracy, safety, relevance, consistency, and latency. Conduct adversarial testing for hallucinations and bias, support alerting and user acceptance testing, integrate tests into CI/CD pipelines, and document coverage and known issues.

Develops quality assurance strategies and automated testing frameworks for GenAI and agentic applications. Responsibilities include Python-based end-to-end testing, prompt regression testing, API, UI, performance, security, and adversarial testing, plus evaluation metrics for accuracy, relevance, safety, consistency, and latency. The role also builds monitoring and alerting systems, maintains test datasets, and establishes QA best practices for AI features.

Designs QA strategies, evaluation frameworks, quality metrics, and automated testing systems for GenAI and agentic applications. Develops Python-based end-to-end tests across APIs, UIs, performance, prompts, and regression scenarios. Performs adversarial testing for hallucinations, bias, and security issues; builds monitoring and alerting systems; maintains golden datasets; and establishes testing best practices for reliable, safe, and trustworthy AI features.

Develop automated QA strategies and evaluation frameworks for GenAI and agentic systems. Build Python-based tests across APIs, UIs, performance, prompts, and regression scenarios. Measure model accuracy, relevance, safety, consistency, and latency; curate test datasets and golden examples; conduct adversarial testing for hallucinations and bias; maintain CI/CD test integration, production alerting support, documentation, and user acceptance testing.

Lead Enterprise Account Executives across the UK, drive revenue and quota attainment, manage pipelines and forecasts, coach sales teams, oversee hiring and retention, and develop strategic enterprise relationships. The role requires expertise in complex SaaS sales, value-based selling, MEDDICC, territory planning, and executive-level engagement. Travel within the region is required as needed.

Lead end-to-end enterprise Anaplan implementations across the UKI market. Responsibilities include project governance, scoping, budgeting, forecasting, billing, risk and resource management, stakeholder engagement, executive communication, escalation handling, project recovery, Agile delivery, consensus building, and portfolio reporting. The role serves as a strategic customer advisor while coordinating internal teams and external partners to deliver successful, scalable solutions.

Lead a DevOps team responsible for building, scaling, securing, and optimizing ML and GenAI infrastructure. Define cloud infrastructure roadmaps, automate provisioning, architect MLOps and LLMOps pipelines, deploy LLMs to production, and manage AI infrastructure budgets through FinOps practices. Implement resource-efficiency measures, observability, incident response, data governance, and security controls while collaborating with Data Science and Engineering leaders.