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Aequor Information Technologies Pvt. Ltd. · Bothell, Washington
Employment Type: W2 Contract
Duration: 12 months
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
1. 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
2. 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
3. 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
4. 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
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 testing, documenting, releasing, maintaining, and troubleshooting software
Experience in life sciences, pharmaceuticals, biotechnology, clinical research, or other regulated environments
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
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
Familiarity with SQL, relational DBs, and other data structures
Familiarity with Airflow, Nextflow, Snakemake, CWL, or other workflow platforms
Understanding of data provenance, privacy, consent, regulated data, GxP considerations, and risk-based software
Interested in this opportunity?
Apply through LinkedIn or get in touch to learn more about the role.
Aequor Technologies LLC is an Equal Opportunity Employer. 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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Details
| Company | Aequor Information Technologies Pvt. Ltd. |
| Location | Bothell, Washington |
| Type | FULL TIME |
| Niche | Full-time |