Senior Applied AI Engineer

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Role purpose

The Senior Applied AI Engineer designs, builds, and operates the AI-driven services that provide core functionality to Documo's document processing platform. You will shape the technical architecture of intelligent processing and machine learning systems, with a focus on LLM-powered systems, including document understanding, structured extraction, retrieval pipelines, and agentic workflows.

You will operate with genuine autonomy, treating technical decisions as product decisions and taking ownership from ambiguous problem statements to measured production outcomes. You will collaborate closely with Product Management, System Architects, DevOps, and Customer Success, serving as a technical lead who mentors less experienced engineers and occasionally engages with customers on complex solution design or escalations.

Role details

• Team: Engineering
• Direct reports: None
• Location: Remote within the United States

Department function

The Engineering Department is crucial in designing, developing, and maintaining the company's software products. The department manages development, infrastructure, operations, quality assurance, and product management, collaborating to provide reliable, secure, and scalable technology solutions.

Its primary objective is to build and sustain software products that satisfy customer needs and align with Documo's long-term business strategy. The department researches and integrates new technologies to enhance internal products and operations and ensures products meet the company's IT, information security, and compliance program requirements.

Department responsibilities

• Provide strategic technical direction for the business.
• Architect, develop, and maintain Documo's software product catalog in collaboration with the Product Team, internal stakeholders, and customers.
• Ensure technology solutions meet high standards for reliability, security, and scalability.
• Maintain technical documentation and procedures for the effective development, maintenance, and support of software products.
• Research and implement new technologies that improve internal operations.
• Implement requirements to satisfy information technology, information security, and compliance programs.
• Provide escalated technical support to customers and internal stakeholders as needed.

Key relationships

Internal: Customer Success, DevOps, Product Management, Quality Assurance, and System Architects.

External: Customer technical and operational stakeholders, engaged as needed for escalations, solution design, and technical integration discussions, typically in partnership with Customer Success and implementation teams.

Responsibilities

AI and LLM systems engineering

• Design, develop, and maintain reliable, scalable, and reproducible LLM-powered services and pipelines for document understanding, structured data extraction, classification, and summarization, following the company's software development life cycle processes.
• Develop agentic and multi-step workflows where they demonstrably outperform simpler approaches, with appropriate guardrails and fallbacks.
• Select the right approach for each problem, including hosted frontier models, fine-tuned open-weight models, or classical machine learning, based on quality, latency, and cost. Implement and optimize fine-tuning where it demonstrably outperforms prompting and retrieval.
• Create and maintain high-quality datasets for model development, fine-tuning, retrieval, and evaluation.
• Collaborate with Product Management and Quality Assurance to ensure machine learning and AI solutions meet customer needs and performance requirements.

Evaluation, observability, and code quality

• Build and maintain evaluation suites for LLM-powered features, including curated golden datasets, automated scoring calibrated against human review, and regression detection, so model, prompt, and pipeline changes ship with evidence rather than intuition.
• Establish observability for production AI services, including request tracing, token and cost accounting, and monitoring for quality drift and failure modes such as malformed or unfounded model outputs.
• Lead code reviews with particular attention to machine-learning- and LLM-specific concerns such as data processing, prompt and model versioning, experiment tracking, evaluation coverage, and production readiness.

Problem solving and optimization

• Provide problem-solving support for machine learning and AI systems, including performance optimization, debugging model and retrieval behavior, and improving pipeline efficiency.
• Optimize production inference across latency, throughput, token and compute cost, batching, caching, model routing, and model right-sizing, treating inference cost as a product constraint.
• Optimize machine learning pipelines for production environments, focusing on scalability, latency, and resource utilization.

Customer engagement and escalation support

• Serve as a technical point of contact for escalated customer issues involving machine learning and AI services, partnering with Customer Success to reproduce, root-cause, fix, and close the loop with affected stakeholders.
• Participate in customer-facing solution design sessions, technical discussions, and integration support when required, representing the team's capabilities to technical and non-technical audiences and translating customer needs into engineering direction.
• Feed lessons from customer engagements back into product, dataset, and system improvements.

Technical thought leadership and mentoring

• Drive complex projects from ambiguous problem statements through design, implementation, and measured rollout, identifying and pursuing high-impact opportunities aligned with company strategy.
• Influence the architecture of machine learning and LLM systems and mentor engineers by sharing best practices, guiding implementation efforts, and providing constructive feedback.

Compensation and benefits

The annual salary range is $120,000-$175,000 USD.

• Generous 401(k) match