(Data & ML Platform) - Technical Interviewer

Intetics

6d

About The Position

Intetics Inc. is a leading American technology company providing custom software application development, distributed professional teams creation, software product quality assessment, and “all-things-digital” solutions, is looking for Technical Interviewers.
Expert Profile Overview
The technical expert should have hands-on experience designing and operating large-scale data / ML platforms, ideally in medical imaging or regulated environments.
They must be able to evaluate both implementation-level skills and system-level decision-making, with a strong focus on traceability, compliance, and production readiness.
Areas of Expertise RequiredData & ML Platform Architecture
Experience building end-to-end data platforms spanning on-prem infrastructure and AWS.
Ability to assess architectural decisions related to scalability, fault tolerance, cost optimization, and data lineage.
Understanding of how to design systems that are FDA-ready by design, not retrofitted.
Medical Imaging & Ingestion Pipelines
Strong familiarity with DICOM, PACS workflows, and tools such as Orthanc.
Ability to assess ingestion and QC strategies for:
CT imaging
Video and C-Arm data
Radiology reports
Understanding of data validation, normalization, and failure handling in clinical pipelines.
Distributed Processing & AWS
Hands-on experience with AWS Batch, preferably with Spot instances.
Ability to evaluate candidate knowledge of:
Job orchestration
Cost-aware scaling
Idempotency and retries
Large-scale batch QC and inference workloads
General AWS proficiency (S3, IAM, networking concepts).
Dataset Versioning & Experiment Tracking
Practical experience with ClearML or comparable tools.
Ability to assess:
Dataset lineage and provenance
Experiment reproducibility
Artifact and metric tracking
Understanding of how these capabilities support regulatory audits.
Training Data Access & Storage Optimization
Experience with Lance or equivalent high-performance data access layers.
Ability to evaluate candidate approaches to:
Fast data loading for training
Incremental dataset updates
Decoupling raw media from derived data
Metadata, Labels & Search
Strong understanding of PostgreSQL-based services for metadata, labels, and predictions.
Ability to assess database schema design for traceability and auditability.
Familiarity with OpenSearch (text/vector) as a plus.
Labeling Workflows
Experience integrating labeling platforms (Encord preferred).
Ability to evaluate candidate understanding of:
RBAC and access control
QC and review workflows
Audit trails
Algorithmic label ingestion and updates
Regulated Environments & Compliance
Solid understanding of 21 CFR Part 11 expectations:
Access control
Audit trails
WORM storage
Data provenance
Experience working with HIPAA / PHI-regulated data.
Ability to identify compliance gaps in proposed architectures.

Requirements

• Data & ML Platform Architecture: Experience building end-to-end data platforms spanning on-prem infrastructure and AWS. Ability to assess architectural decisions related to scalability, fault tolerance, cost optimization, and data lineage. Understanding of how to design systems that are FDA-ready by design, not retrofitted.
• Medical Imaging & Ingestion Pipelines: Strong familiarity with DICOM, PACS workflows, and tools such as Orthanc. Ability to assess ingestion and QC strategies for: CT imaging, Video and C-Arm data, Radiology reports. Understanding of data validation, normalization, and failure handling in clinical pipelines.
• Distributed Processing & AWS: Hands-on experience with AWS Batch, preferably with Spot instances. Ability to evaluate candidate knowledge of: Job orchestration, Cost-aware scaling, Idempotency and retries, Large-scale batch QC and inference workloads, General AWS proficiency (S3, IAM, networking concepts).
• Dataset Versioning & Experiment Tracking: Practical experience with ClearML or comparable tools. Ability to assess: Dataset lineage and provenance, Experiment reproducibility, Artifact and metric tracking. Understanding of how these capabilities support regulatory audits.
• Training Data Access & Storage Optimization: Experience with Lance or equivalent high-performance data access layers. Ability to evaluate candidate approaches to: Fast data loading for training, Incremental dataset updates, Decoupling raw media from derived data
• Metadata, Labels & Search: Strong understanding of PostgreSQL-based services for metadata, labels, and predictions. Ability to assess database schema design for traceability and auditability.
• Labeling Workflows: Experience integrating labeling platforms (Encord preferred). Ability to evaluate candidate understanding of: RBAC and access control, QC and review workflows, Audit trails, Algorithmic label ingestion and updates
• Regulated Environments & Compliance: Solid understanding of 21 CFR Part 11 expectations: Access control, Audit trails, WORM storage, Data provenance. Experience working with HIPAA / PHI-regulated data. Ability to identify compliance gaps in proposed architectures.

Nice To Haves

• Familiarity with OpenSearch (text/vector) as a plus.
• Senior / Principal Data Engineer, ML Platform Engineer, or MLOps Engineer.
• Prior experience in healthcare, medical imaging, or regulated ML systems is strongly preferred.
• Comfortable challenging candidates and defending technical decisions in front of stakeholders.

Responsibilities

• Participate in technical interviews (system design + deep dive).
• Ask scenario-based questions focused on real production and regulatory challenges.
• Evaluate candidate answers for: Practical experience vs. theoretical knowledge, Trade-off awareness, Risk identification and mitigation
• Review take-home tasks or architectural diagrams if applicable.
• Provide clear, structured written feedback with a hire / no-hire recommendation.

What This Job Offers

• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 501-1,000 employees