As the AI Revenue Engineer, you will be responsible for developing, deploying, and managing AI-driven agents that streamline sales, marketing, and revenue operations. Key Responsibilities - Build and run autonomous agent systems that execute go-to-market workflows, including research, outreach, data enrichment, and reporting. - Oversee the AI operations toolchain end to end — identifying opportunities for automation, designing and deploying agents, and continuously optimizing performance. - Collaborate directly with leadership to address high-impact operational challenges and drive measurable results. - Maintain and enhance agentic systems integrated with platforms such as HubSpot, Clay, Slack, Notion, and Attention.​

As the AI Revenue Engineer, you will be responsible for developing, deploying, and managing AI-driven agents that streamline sales, marketing, and revenue operations. Key Responsibilities - Build and run autonomous agent systems that execute go-to-market workflows, including research, outreach, data enrichment, and reporting. - Oversee the AI operations toolchain end to end — identifying opportunities for automation, designing and deploying agents, and continuously optimizing performance. - Collaborate directly with leadership to address high-impact operational challenges and drive measurable results. - Maintain and enhance agentic systems integrated with platforms such as HubSpot, Clay, Slack, Notion, and Attention.​

Role Description Design, develop, and integrate AI-driven features within industrial control systems and related software platforms. - Implement AI-assisted tooling and automation to improve engineering productivity, testing, diagnostics, or operator workflows. - Collaborate with software developers, hardware engineers, and product stakeholders to identify practical AI use cases aligned with operational needs. - Develop and maintain services that integrate with LLM APIs, local AI models, or intelligent automation frameworks. - Evaluate model performance, reliability, latency, and cost considerations in production environments. - Design safeguards and validation layers to ensure AI-generated outputs meet operational safety and correctness standards. - Contribute to data preparation, structured logging, and telemetry systems that support AI-enabled workflows. - Participate in architectural discussions to ensure AI components integrate cleanly within modular, service-oriented systems. - Utilize AI development tools to accelerate coding, prototyping, documentation, and research while validating all outputs for correctness and maintainability. - Participate in Agile/Scrum ceremonies including sprint planning, backlog refinement, and retrospectives. - Provide accurate estimates, surface technical risks early, and communicate trade-offs clearly. - Support troubleshooting, performance tuning, and continuous improvement of AI-enabled features. - Perform other related duties and special projects, as assigned. Qualifications - Demonstrated ability to design and implement production-quality software systems. - Experience integrating external APIs, services, or SDKs into software applications. - Strong understanding of software architecture, modular design, and service-oriented systems. - Working knowledge of data handling, structured formats, and secure API communication. - Ability to evaluate AI-generated output critically for accuracy, bias, safety implications, and operational risk. - Strong verbal and written communication skills, including the ability to explain AI-related technical concepts to cross-functional stakeholders. - Ability to work effectively in a cross-functional, matrixed organization including software, hardware, controls, and operations teams. - Ability to manage multiple initiatives in a fast-paced environment while maintaining quality standards. - Experience using AI-assisted development tools in a professional setting. - Represent the Company and its values with integrity while maintaining high professional and ethical standards in all interactions. Requirements - Bachelor’s Degree in Computer Science, Computer Engineering, Software Engineering, Data Science, or related field (or equivalent practical experience). - 3–6 years of professional software development experience. - Strong proficiency in C#, C++, Python, or comparable programming languages. - Experience working within an Agile development framework. - Experience integrating AI APIs, automation tools, or intelligent systems into applications. Preferred Qualifications - Experience working with LLM APIs, prompt engineering, embeddings, or vector databases. - Experience building internal AI tooling or workflow automation systems. - Exposure to real-time or hardware-integrated systems. - Familiarity with industrial or Oil & Gas environments. - Experience implementing monitoring, evaluation, or guardrails for AI-enabled systems. - Understanding of latency, throughput, and cost optimization considerations for AI workloads. Additional Details The Intermediate AI Developer must work with an emphasis on individual safety and safety for others, including coworkers and third parties. Work is primarily performed in a climate-controlled office or remote environment. AI-enabled systems may support software that interfaces with physical equipment; therefore, reliability and operational awareness are essential. The employee is regularly required to sit, stand, or use computer equipment for extended periods.

• Architect and deploy LLM-powered and agentic AI solutions that transform claims intake, policy interpretation, fraud detection, and resolution workflows. • Design end-to-end retrieval-augmented generation (RAG) systems leveraging enterprise knowledge bases, policy documents, SOPs, and historical claims data. • Build autonomous and semi-autonomous agents capable of reasoning, planning, and executing multi-step claims processes. • Develop stateful workflow orchestration layers that manage context, memory, and task sequencing across interactions. • Implement planning and reflection loops that decompose complex claims scenarios into structured subtasks. • Enable dynamic tool use through function calling and secure API integrations with claims systems, CRM platforms, document repositories, and analytics tools. • Develop document intelligence pipelines using LLMs for summarization, entity extraction, classification, validation, and timeline reconstruction. • Design structured prompt frameworks that enforce deterministic outputs and domain-aware reasoning. • Build multi-agent systems that coordinate document review, coverage analysis, compliance checks, and decision support. • Implement human-in-the-loop checkpoints for escalation, review, and override of AI-driven decisions. • Develop guardrails, output validation layers, and hallucination mitigation strategies. • Enforce structured outputs using schemas, type validation, and deterministic post-processing logic. • Optimize token consumption, inference latency, and cloud infrastructure costs. • Deploy scalable AI microservices using containerization and cloud-native architectures. • Implement monitoring for model drift, retrieval quality degradation, reasoning failures, and workflow breakdowns. • Maintain detailed audit logs of model decisions, agent reasoning steps, and tool executions. • Develop evaluation frameworks to test reasoning accuracy, workflow completion rates, and system reliability. • Collaborate with data engineering to build embedding pipelines, feature stores, and vector indexing strategies. • Ensure compliance with Responsible AI standards, data privacy regulations, and enterprise governance policies. • Partner with claims operations leadership to embed AI capabilities directly into adjuster and supervisor workflows. • Measure business impact through cycle-time reduction, automation coverage, fraud detection lift, and operational efficiency gains.