Senior AI Business Automation Engineer (Dot Compliance) - Technology IT

About Signant Health

At Signant Health, we help bring life-changing treatments to patients faster. We are a global evidence generation company that supports clinical trials with smart technology, scientific expertise, and hands-on operational support — so better data leads to better decisions in healthcare. We embrace AI and advanced technologies to enhance every aspect of what we do, from data analysis to operational efficiency.

Our teams work at the intersection of science, technology, and patient experience, delivering digital solutions powered by AI innovation that make clinical trials more efficient, more accurate, and more accessible around the world. Trusted by leading pharmaceutical companies and CROs, our platforms and services support studies across more than 90 countries and have contributed to hundreds of new drug approvals.

If you are motivated by meaningful work, global impact, and innovation in clinical research and digital health — including the opportunity to work with cutting-edge AI technologies — you will find purpose and opportunity at Signant Health.

About the Role
Signant Health is seeking a Senior AI Business Automation Engineer who works in two complementary modes. In one mode, you embed directly with functional Subject Matter Experts — sitting shoulder-to-shoulder with the people doing the work, understanding their workflows including the workarounds and group knowledge that never appear in process documents, and then designing, prototyping, and delivering the custom technical solutions that transform how those teams operate. In the other mode, you work on a small, focused delivery squad — sometimes leading, sometimes contributing — tasked with shipping AI-driven automation into a business unit at pace.

The work in both modes is the same in spirit: identify business processes ripe for reinvention, rebuild them with an AI-first mindset, and put working code in front of real users quickly. The role lives inside IT and carries the full weight of our infrastructure, data, integration, automation, and information security standards required of a clinical-trial technology provider. Your code ships to production through regular, incremental delivery against real workflow data, not to a slide deck. Your measure of success is the number of workflows you have permanently transformed and the extent to which the business teams you work with start every task with an AI tool.

KEY ACCOUNTABILITIES – Function

Flex between two operating modes: embedded directly with an assigned cohort of business Subject Matter Experts, observing how their work actually happens — the workarounds, the shared spreadsheets, the group knowledge — to identify the highest-leverage workflows to reinvent with an AI-first design; and contributing to or leading small, focused delivery squads tasked with shipping AI-driven automation into a business unit at pace. In either mode, the discipline is the same: get close to the real work, find the leverage, and rebuild rather than automate as-is.
Design, prototype, and deliver working solutions — custom tools, AI agents, automations, and reusable skills — tailored to each engagement’s specific workflows, using a cadence of regular, incremental delivery against real workflow data so value is realised continuously rather than at the end of a project.
Coach and partner with the business users you work alongside — whether SMEs in an embedded cohort or stakeholders on a squad engagement — taking them through a progressive journey: from awareness, to first win, to regular AI integration, to full workflow transformation.
Develop and deploy AI agents leveraging large language models (LLMs) and Model Context Protocol (MCP) integrations, end-to-end automations across REST APIs, webhooks, and third-party SaaS, and secure data pipelines — whatever the workflow requires, built to enterprise standards.
Build every solution for handoff from day one — chosen technology, documentation, observability, and runbooks designed so other engineers can operate, diagnose, and evolve the solution without ongoing involvement from you.
Recognise patterns across engagements and systematically scale what works. A tool built for one SME or team should become reusable for their peers; a transformation pattern proven on one squad should be documented so other engineers can apply it elsewhere.
Translate observed business problems into technical solutions, and share wins visibly within your cohort and with leadership to build momentum, inspire other teams, and celebrate progress — with ROI, effort, and risk articulated honestly enough that prioritisation decisions are easy to make.
Participate in end-to-end testing and quality validation of AI, automation, and integrated solutions, ensuring reliable performance, accuracy, and alignment with business requirements prior to production deployment.
Use AI development tools fluently throughout the build lifecycle — AI coding assistants (e.g. Claude Code, Cursor, GitHub Copilot) for rapid prototyping and refactoring, LLMs for mining workflow signals from tickets and transcripts, and AI-assisted drafting of solution designs, runbooks, and tests — while applying rigorous AI-specific evaluation discipline (golden datasets, eval harnesses, regression suites, hallucination and grounding checks, human-in-the-loop checkpoints) to validate accuracy, safety, and business alignment before production deployment.
Ensure automation solutions meet enterprise standards for security, scalability, and maintainability, including working knowledge of SOC 2 and ISO 27001 compliance requirements for automation and data handling.
Mentor both directions: upskill the business users in your engagements so they can build and maintain their own AI-assisted workflows, and support junior engineers and automation analysts inside IT by establishing reusable patterns, solution designs, runbooks, and executive-ready status reports.
Participate in and extend the internal automation platforms so they can support ever more advanced workflows, reusable modules, and execute more business processes autonomously.

KNOWLEDGE, SKILLS & ATTRIBUTES

Essential:

Bachelor’s degree in Computer Science, Information Systems, Engineering, or equivalent practical experience.
5+ years of experience in enterprise automation, software development, or IT engineering.
Hands-on experience designing and operating workflow automation (self-hosted or cloud) with REST API and webhook integrations across enterprise SaaS platforms.
2+ years of experience building with the modern AI development toolkit: LLM APIs (Anthropic, OpenAI, or equivalent), Model Context Protocol (MCP) servers and clients, agent frameworks and agentic workflow patterns, AI coding assistants used as a daily driver (e.g. Claude Code, Cursor, GitHub Copilot), structured prompt engineering, retrieval-augmented generation grounded in enterprise data sources, and eval harnesses for measuring agent accuracy and safety in production.
Proficiency in JavaScript/TypeScript and Python; experience with Git, CI/CD pipelines, and cloud platforms (AWS, Azure, or GCP).
Ability to communicate technical solutions clearly to both engineering teams and executive stakeholders; demonstrated experience authoring business cases, solution designs, and implementation roadmaps.
Strong analytical and problem-solving skills with a passion for continuous improvement, process optimization, and delivering measurable business outcomes through technology.
Demonstrated ability to discover real workflows by working closely with practitioners — surfacing the workarounds, shared spreadsheets, and undocumented group knowledge that determine whether an AI solution works on real edge cases.
Comfortable operating in small, high-velocity delivery teams — able to step into either a lead or contributor role on a squad of 2–4 engineers, hold a clear delivery line under time pressure, and partner well with stakeholders who expect working software in front of users quickly.

Desirable:

Relevant professional certifications in IT service management, project management, or enterprise automation platforms (e.g. ITIL, PMP, or platform-specific certifications).
Experience with iPaaS integration platforms (Boomi, MuleSoft, or Workato) connecting enterprise systems including ERP, HRIS, CRM, and ITSM tooling.
Global experience collaborating with distributed teams to deliver enterprise technology initiatives.
Experience using AI itself to discover and redesign workflows — for example, analysing transaction histories, ticket logs, or content repositories with LLMs to surface bottlenecks, or building agentic prototypes in hours to test a hypothesis before committing to a production build.

Platform-Specific Requirements – Dot Compliance Track

Essential:

3+ years of hands-on experience configuring and extending the Dot Compliance eQMS (built on the Salesforce Platform), including Quality Process workflows such as CAPA, Deviations, Audits, Change Control, Document Control, and Training Management.
Proven experience building and deploying custom Salesforce Apex triggers/classes, Lightning Web Components, and Flow automations within a validated Dot Compliance production environment.
Proficiency in Apex and SOQL, in addition to JavaScript/TypeScript and Python, for scripting and integration work on the Salesforce/Dot Compliance platform.
Experience integrating AI/LLM APIs into Dot Compliance quality workflows (e.g. automated CAPA root-cause drafting, deviation triage, audit-readiness summarisation) grounded in regulated GxP data.

Desirable:

Experience with GxP-regulated quality systems and computer system validation (CSV/CSA) practices in a life sciences or clinical-trial environment.
Salesforce Platform Developer I/II certification and/or Dot Compliance administrator or configuration credentials.