(Data & ML Platform) - Technical Interviewer

Intetics Georgia
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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.
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