AI Solutions Engineer

Employment Type: W2 ContractDuration: 12 monthsThe AI Solutions Engineer will design, build, test, and maintain reusable software, data pipelines, automation, and AI-enabled applications for Translational Medicine.The successful candidate will translate requirements from business, scientific, and operational stakeholders into reliable data products, automated quality-control workflows, dashboards, and agentic AI applications.The engineer will work closely with data scientists, product owners, and enterprise technology teams to move prioritized solutions from requirements through deployment and maintenance.The role is focused on developing scalable technical products, not conducting primary data analysis or building isolated, study-specific tools.Solutions must be secure, well documented, version-controlled, reproducible, and aligned with enterprise and industry data, privacy, cybersecurity, quality, and responsible AI requirements.

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Key Responsibilities1. AI Application and Software EngineeringDesign, develop, test, document, and maintain AI-enabled applications, dashboards, APIs, and/or workflow-automation solutions using approved models and platformsDevelop modular components that can be reused across programs, studies, disease areas, and workflow typesCreate user-friendly applications that make governed data and computational capabilities accessible to scientific and operational usersEvaluate prototypes for feasibility, reliability, scalability, maintainability, and readiness for broader deploymentImplement human review, source visibility, feedback mechanisms, access controls, and appropriate transparency measures for AI-enabled products2. Data Engineering and Automated Quality ControlDevelop and maintain data-ingestion, transformation, reconciliation, validation, and quality-control workflows for clinical biomarker dataMaintain and expand existing R-based software, ETL scripts, and automated QC capabilitiesTranslate data-transfer specifications, scientific requirements, and business rules into executable codeDevelop configurable workflows that reduce repetitive setup and manual intervention across datasets and studiesProduce clear reports, processing logs, QC outputs, and traceable records for scientific and operational reviewPreserve source values, surface unresolved exceptions, and ensure workflows fail safely3. Software Quality and ReproducibilityWrite maintainable, modular, tested, and appropriately documented codeUse approved source-control, branching, release, and versioning practices with GitMaintain traceability among requirements, source data, code versions, tests, outputs, and releasesContribute to monitoring, logging, error handling, and operational support documentationSupport risk-appropriate peer review and independent code validation4. Responsible AI and GovernanceDesign solutions in accordance with applicable responsible AI, privacy, cybersecurity, data-rights, third-party, quality, and regulatory requirementsDocument solution architecture, data sources, data flows, intended users, intended use, limitations, and human-oversight mechanismsSupport required AI risk triage, mitigation documentation, and technical reviewsImplement and test agreed technical controlsEscalate governance, privacy, security, data-rights, validation, or regulatory questions to the accountable *** expertsRequired Qualifications:Education and ExperienceBachelor's or master's degree in computer science, software engineering, data science, bioinformatics, computational biology, biomedical engineering, or a related quantitative fieldAt least two years of professional experience developing data-intensive software, scientific applications, data pipelines, AI-enabled applications, or automation solutions in a production settingDemonstrated experience delivering software beyond exploratory notebooks or one-time analysesExperience translating stakeholder needs into technical requirementsExperience testing, documenting, releasing, maintaining, and troubleshooting softwareExperience in life sciences, pharmaceuticals, biotechnology, clinical research, or other regulated environmentsRequired Technical SkillsProficiency in Python for application development, automation, data processing, API integration, and testingProficiency in R, including package development or maintenance, ETL workflows, testing, and documentationStrong command of Git and GitHub, including branching and pull requests as well as release and version managementExperience with code documentation, automated testing, logging, debugging, and error handlingExperience developing ETL, data-validation, reconciliation, or workflow-automation solutionsWorking knowledge of Linux development environmentsAbility to communicate technical concepts to scientific and operational stakeholders
Preferred QualificationsExperience with clinical, multi-omics, imaging, pathology, or real-world datasetsExperience building generative-AI or agentic applications, structured outputs, evaluation frameworks, or human-in-the-loop approachesExperience with Microsoft Copilot StudioExperience with AWS and/or DatabricksExperience with Docker, CI/CD pipelines, and deployment automationFamiliarity with SQL, relational DBs, and other data structuresExperience with REST APIs and MCPFamiliarity with Airflow, Nextflow, Snakemake, CWL, or other workflow platformsUnderstanding of data provenance, privacy, consent, regulated data, GxP considerations, and risk-based software
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Aequor Technologies LLC is an Equal Opportunity Employer. xzbcwsv All qualified applicants will be considered without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected status.
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