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Partner with enterprise customers to identify AI use cases, architect and implement custom applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into solution recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and business outcomes.

Lead independent AI research on large language models and agentic systems, from hypothesis through training, evaluation, and production deployment. Develop post-training methods, reasoning and planning capabilities, evaluation benchmarks, synthetic data pipelines, and enterprise AI improvements. Publish at leading conferences, shape model strategy, and mentor researchers and engineers.

Generate outbound pipeline from target enterprise accounts by researching prospects, developing value hypotheses, executing multi-touch account-based campaigns, qualifying leads, and setting meetings for sales representatives. The role partners with account executives, marketing, and sales; improves prospecting processes; and maintains accurate Salesforce data. It requires strong written communication, an experimental mindset, and demonstrated success exceeding outbound pipeline goals in a B2B SaaS startup.

Architect generative AI solutions for Fortune 500 prospects and customers. Lead technical discovery, design scalable enterprise solutions using WRITER’s platform and APIs, develop proofs of concept and demonstrations, advise executive stakeholders, and guide enterprise AI architecture roadmaps. Collaborate with product and engineering teams to influence the roadmap, educate customers and partners, and drive successful adoption of AI capabilities.

Build and operate highly available infrastructure for WRITER’s enterprise AI platform. Responsibilities include designing cloud and Kubernetes systems, automating infrastructure with Python or Go, managing Terraform and Helm, leading incident response and postmortems, defining SLOs, improving observability and reliability, and owning infrastructure initiatives end to end. The role also requires daily use of AI-assisted development workflows and collaboration with product, security, and engineering teams.

Leads high-impact, cross-functional product initiatives from discovery through launch and iteration. Shapes multi-quarter strategy and roadmaps, engages enterprise customers, partners with engineering and design on AI and platform products, defines success metrics, aligns stakeholders, and mentors product managers. The role requires technical fluency in LLMs, agents, data, and architecture, along with strong strategic judgment, execution, communication, and customer empathy.

Build and deploy scalable AI applications, intelligent agents, and generative AI features in production. Collaborate with research, data science, and product teams; evaluate emerging AI technologies; and implement responsible AI practices including bias detection, explainability, and safety alignment. The role requires production machine learning experience, Python and AI framework expertise, cloud and MLOps knowledge, and strong software engineering skills.

Partner with enterprise customers to identify AI use cases, validate feasibility, and implement tailored solutions using WRITER’s platform, APIs, and Knowledge Graph. Architect applications, templates, and integrations; explain AI capabilities to technical and executive audiences; collaborate with Product and Engineering; develop scalable integration processes and documentation; and drive customer adoption, expansion, and measurable business outcomes.

Build and scale AI agent systems that autonomously execute workflows and integrate with enterprise data and services. Develop agent infrastructure, APIs, memory, orchestration, security, state management, and asynchronous workflows. Integrate vector databases, retrieval systems, backend services, and external APIs. Ensure reliability through monitoring, observability, testing, CI/CD, and fault tolerance while collaborating with product, research, data, and UX teams.

Build secure, scalable AI integration platforms connecting enterprise systems, tools, data sources, and applications. Design backend APIs, distributed services, authentication and authorization systems, MCP clients and servers, and cross-system orchestration. Establish reliability practices including SLOs, observability, incident readiness, and performance optimization. Partner with product and engineering leaders on technical strategy, mentor engineers, lead architecture reviews, and own delivery through production.