AI Model Quality Engineer

Job Title
AI Model Quality Engineer - Healthcare (Wellness & Digital Health)
Location: Bangalore
Experience
6 - 12+ years overall experience
3+ years in ML / AI model testing, validation, or governance

Role Overview
We are seeking an AI Model Quality Engineer to ensure trustworthiness, safety,reliability, and regulatory compliance of AI/ML models used in our platform.
This role sits at the intersection of Quality Engineering, Data Science, Responsible AI, and Healthcare Compliance.
You will serve as the independent validation authority within the Model DevelopmentLife Cycle (MDLC). Functioning as a strategic partner to-but distinct from-the ModelDevelopment team, you will ensure strict separation of concerns by owning theobjective validation gates between "ML Model Testing" and "Clinical Handoff". You willbe responsible for certifying that models meet all defined acceptance criteria before theyare exposed to clinical evaluators or production traffic.

Key Responsibilities

1. Model Quality, Validation, & "Shift Left" Strategy

• Requirements Definition: Partner with Clinical and Product teams during theRequirement Gathering phase to translate clinical goals into quantifiabletechnical acceptance criteria (e.g., defining specific thresholds for FalseNegatives, bias limits, or hallucination rates).
• Golden Set Management: Create, maintain, and secure "Golden Datasets" and"Adversarial Test Sets" that are isolated from the training process, ensuring trueout-of-sample validation.
• Test Execution: Design and execute test strategies for AI/ML models (predictive,classification, recommendation, and GenAI/LLM-based systems).
• Comprehensive Validation: Validate model behavior across accuracy, bias,robustness, stability, and safety using independent test pipelines.

2. Engineering & Clinical Handoffs

• API Contract Validation: Validate model artifacts (containers, APIs) during theNon-production Model Deploy phase to ensure latency, throughput, and errorhandling meet Engineering Service Level Agreements (SLAs) before handoff.
• Clinical Pre-Screening: Execute automated "Red Teaming" and safety checks(e.g., PII leakage, hallucination triggers) to sanitize models before they enter theClinical Testing phase, maximizing the efficiency of Clinical SME time.
• Release Gating: Own the final "Go/No-Go" decision for model promotion basedon pre-defined quality gates.
  
  

3. Drift Detection, Monitoring & Feedback Loops

• Automated Feedback Loops: Design and implement automated pipelines thattrigger Retraining Feedback Loops when post-deployment monitoring detectsdrift or performance degradation.
  
• Drift Mechanisms: Design and test data drift, concept drift, and prediction driftdetection mechanisms.
  
• Data Sanity Checks: Implement upstream validation to catch data quality issues(missing values, schema changes) before model training begins.
  
• Alerting: Define thresholds, alerts, and runbooks for drift remediation androllback strategies.

4. Explainability & Transparency

• Validate model explainability using tools such as SHAP, LIME, Integrated Gradients, or equivalent.
  
• Ensure explanations are clinically meaningful, auditable, and regulator-ready.
  
• Partner with Data Science teams to define explainability acceptance criteria.

5. Hallucination & GenAI Safety (if LLMs are used)

• Design test scenarios to detect hallucinations, unsafe outputs, and medicalmisinformation.
  
• Validate grounding mechanisms (RAG, citations, confidence scoring).
  
• Ensure models do not generate diagnostic or treatment advice beyond approvedscope.

6. Healthcare Compliance & Responsible AI

• Ensure AI systems align with HIPAA (PHI protection), FDA SaMD principles, andResponsible AI guidelines.
• Participate in model risk reviews, audits, and governance forums.
• Maintain documentation for model cards, data sheets, and audit artifacts.

Required Qualifications
Core Skills

• Strong background in Quality Engineering / Validation / Test Automation.
• Hands-on experience testing ML models and/or LLM-based systems.
• Solid understanding of:
  o Model lifecycle (training validation deployment monitoring)
  o Bias, fairness, drift, overfitting, and calibration
• Experience with Python, notebooks, and ML testing frameworks.

AI / ML Knowledge

• Familiarity with:
  o Supervised & unsupervised ML models
  o Model explainability techniques (SHAP, LIME, etc.)
  o Evaluation metrics for ML and GenAI systems
  o ML testing frameworks
• Experience with model monitoring tools or custom pipelines.

Preferred / Nice-to-Have

• Experience testing GenAI / LLM systems (RAG, prompt evaluation,hallucination detection).
• Exposure to FHIR, EHR integrations, or healthcare data standards.
  
• Familiarity with Model Risk Management (MRM) or AI governanceframeworks.
  
• Experience with cloud ML platforms (Azure ML, AWS SageMaker, GCP).
  
• Certifications in AI, Data, Cloud, or Quality Engineering.