AI and Automation Engineer - Business Systems

About the job

Responsibilities

• Design, build, test, deploy, and support production AI agents, orchestration services, enterprise applications, and reusable platform components.
• Create reusable patterns for agents, tools, APIs, Model Context Protocol servers, prompts, retrieval, evaluations, and human-review workflows.
• Design and support secure connections between AI services and approved enterprise platforms.
• Build reliable integrations using APIs, MCP, webhooks, event-driven services, SFTP, and enterprise integration platforms.
• Own operational requirements for internal and third-party components, including credential management, access reviews, patching, monitoring, incident response, and recovery.
• Evaluate models, agent frameworks, connectors, and enterprise platforms through structured proofs of concept and technical recommendations.
• Assess accuracy, security, reliability, integration fit, user experience, latency, operating cost, and vendor viability before production adoption.
• Build evaluation datasets, automated and human grading methods, release thresholds, and ongoing monitoring for deployed solutions.
• Diagnose production failures, identify root causes, and implement durable improvements to code, prompts, data, controls, and operating procedures.
• Partner with business units to identify high-value use cases and translate requirements into controlled solutions.
• Deliver solutions for financial close, reporting, forecasting, procurement, billing, compliance monitoring, and other enterprise workflows.
• Establish clear business ownership, user feedback loops, training, and adoption plans for production workflows.
• Use metrics like task success, accuracy, cycle time, adoption, trust, and support demand to guide iteration and scaling decisions.
• Deliver work through a Scrum-based operating model, participating in all agile ceremonies.
• Convert business needs into epics, user stories, technical tasks, acceptance criteria, evaluation plans, and sequenced releases.
• Break complex initiatives into testable increments and communicate scope, dependencies, risks, tradeoffs, and delivery status clearly.
• Maintain a strong definition of done covering design approval, peer review, testing, security and control validation, documentation, deployment readiness, and operational handoff.
• Translate Security, Privacy, SOX, and SSDLC requirements into technical architecture, application controls, and operating procedures.
• Implement least privilege, segregation of duties, logging, retention, change control, evidence collection, and periodic access review.
• Require deterministic validation and reconciliation when AI or automation touches financially material data or calculations.
• Support control walkthroughs, testing, audits, risk assessments, and remediation.

Requirements

• 8+ years of experience in software, platform, integration, solution engineering, enterprise applications, or related technical roles.
• Meaningful hands-on production ownership in complex environments.
• Strong Python and TypeScript or JavaScript skills.
• Practical experience building APIs, services, and enterprise applications.
• Hands-on experience building production AI systems using agents, tool use, retrieval, structured outputs, evaluations, and monitoring.
• Practical experience with leading LLM platforms and agent frameworks (e.g., OpenAI, Anthropic, Gemini, LangChain, Semantic Kernel).
• Experience designing integrations with MCP or comparable tool protocols, enterprise authentication, permission-aware access, and distributed-system reliability patterns.
• Demonstrated ability to design and build complete solutions.