(Data & ML Platform) - Technical Interviewer
TypeFull-time job
LocationUkraine
Posted3 hours ago
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 Required
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.
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.
Interview Responsibilities
The technical expert will be expected to:
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.
Ideal Background of the Expert
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.
Originally posted on Himalayas
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 Required
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.
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.
Interview Responsibilities
The technical expert will be expected to:
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.
Ideal Background of the Expert
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.
Originally posted on Himalayas
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