AT
AI Solutions Engineer
Aequor Technologies LLC
- Location not stated
- 20 hours ago
- AI
- ETL
- Git
- triage
- Python
- GitHub
- Linux
- Machine Learning
- AWS
- Databricks
- Docker
- CI/CD
- SQL
- REST API
- MCP
- Airflow
- Snakemake
- GxP
20 hours ago
Worker type: Contractor / Contingent Worker
Organization: *** Oncology, Translational Medicine Portfolio Integration
Team: TM AI Strategy & Integration
Reports to: Senior TM AI Strategy & Integration Manager
Location: United States, multiple locations
Duration: 12 months
Role Summary
The 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.
Key Responsibilities
AI Application and Software Engineering
Design, develop, test, document, and maintain AI-enabled applications, dashboards, APIs, and/or workflow-automation solutions using approved models and platforms.
Develop modular components that can be reused across programs, studies, disease areas, and workflow types.
Create user-friendly applications that make governed data and computational capabilities accessible to scientific and operational users.
Evaluate prototypes for feasibility, reliability, scalability, maintainability, and readiness for broader deployment.
Implement human review, source visibility, feedback mechanisms, access controls, and appropriate transparency measures for AI-enabled products.
Data Engineering and Automated Quality Control
Develop and maintain data-ingestion, transformation, reconciliation, validation, and quality-control workflows for clinical biomarker data.
Maintain and expand existing R-based software, ETL scripts, and automated QC capabilities.
Translate data-transfer specifications, scientific requirements, and business rules into executable code.
Develop configurable workflows that reduce repetitive setup and manual intervention across datasets and studies.
Produce clear reports, processing logs, QC outputs, and traceable records for scientific and operational review.
Preserve source values, surface unresolved exceptions, and ensure workflows fail safely.
Software Quality and Reproducibility
Write maintainable, modular, tested, and appropriately documented code.
Use approved source-control, branching, release, and versioning practices with Git.
Maintain traceability among requirements, source data, code versions, tests, outputs, and releases.
Contribute to monitoring, logging, error handling, and operational support documentation.
Support risk-appropriate peer review and independent code validation.
Responsible AI and Governance
Design solutions in accordance with applicable responsible AI, privacy, cybersecurity, data-rights, third-party, quality, and regulatory requirements.
Document solution architecture, data sources, data flows, intended users, intended use, limitations, and human-oversight mechanisms.
Support required AI risk triage, mitigation documentation, and technical reviews.
Implement and test agreed technical controls.
Escalate governance, privacy, security, data-rights, validation, or regulatory questions to the accountable *** experts.
Required Qualifications
Education and Experience
Bachelor's or master's degree in computer science, software engineering, data science, bioinformatics, computational biology, biomedical engineering, or a related quantitative field.
At least two years of professional experience developing data-intensive software, scientific applications, data pipelines, AI-enabled applications, or automation solutions in a production setting.
Demonstrated experience delivering software beyond exploratory notebooks or one-time analyses.
Experience translating stakeholder needs into technical requirements.
Experience testing, documenting, releasing, maintaining, and troubleshooting software.
Experience in life sciences, pharmaceuticals, biotechnology, clinical research, or other regulated environments.
Required Technical Skills
Proficiency in Python for application development, automation, data processing, API integration, and testing.
Proficiency in R, including package development or maintenance, ETL workflows, testing, and documentation.
Strong command of Git and GitHub, including branching and pull requests as well as release and version management.
Experience with code documentation, automated testing, logging, debugging, and error handling.
Experience developing ETL, data-validation, reconciliation, or workflow-automation solutions.
Working knowledge of Linux development environments.
Ability to communicate technical concepts to scientific and operational stakeholders.
Preferred Qualifications
Experience with clinical, multi-omics, imaging, pathology, or real-world datasets.
Experience building generative-AI or agentic applications, structured outputs, evaluation frameworks, or human-in-the-loop approaches.
Experience with Microsoft Copilot Studio.
Experience with AWS and/or Databricks.
Experience with Docker, CI/CD pipelines, and deployment automation.
Familiarity with SQL, relational DBs, and other data structures.
Experience with REST APIs and MCP.
Familiarity with Airflow, Nextflow, Snakemake, CWL, or other workflow platforms.
Understanding of data provenance, privacy, consent, regulated data, GxP considerations, and risk-based software validation.
AI Solutions Engineer · Aequor Technologies LLC