Job Description :

We are seeking an AI Model Testing & Validation Engineer with deep expertise in validating AI/ML models and extensive experience with Bluetooth-enabled connected systems. This role requires technical leadership in defining validation strategies for AI models deployed on edge and embedded devices, ensuring performance, reliability, safety, and scalability under real-world connectivity constraints.

Key Responsibilities :

Core Competencies & Skills :

- Expert in AI/ML model testing and validation across the full lifecycle, from experimentation to production deployment.

- Advanced experience validating ML, Deep Learning, and Edge AI models for accuracy, robustness, generalization, and drift.

- Strong expertise in model evaluation metrics, statistical validation, cross-validation, and error analysis.

- Proven ability to design automated AI validation frameworks and reusable test harnesses in Python.

- Deep understanding of bias, fairness, explainability, and AI safety validation (SHAP, LIME, interpretability techniques).

Bluetooth & Connectivity Expertise :

- Extensive hands-on experience with Bluetooth Classic and Bluetooth Low Energy (BLE).

- Strong knowledge of Bluetooth protocols, profiles, pairing, security, power management, and coexistence.

- Expertise in validating AI model performance under real-world Bluetooth conditions (latency, jitter, packet loss, interference, reconnections).

- Experience with interoperability testing across chipsets, stacks, OS versions, and devices.

- Skilled in protocol-level debugging and connectivity performance analysis.

Edge AI & Embedded Systems :

- Experience validating on-device AI inference on constrained systems (MCUs, DSPs, NPUs).

- Expertise in testing quantized, compressed, and optimized models for latency, power, and memory efficiency.

- System-level understanding of AI integration with firmware, drivers, and connectivity stacks.

- Familiarity with RTOS, embedded Linux, and edge accelerators.

MLOps, Quality & Governance :

- Strong experience with MLOps pipelines, CI/CD, model versioning, monitoring, and drift detection.

- Ability to define validation standards, acceptance criteria, and go/no-go metrics.

- Experience producing validation documentation for audits, certifications, and compliance reviews.

- Knowledge of Responsible AI and model risk management practices.

Nice to Have :

- Experience in medical devices, wearables, or IoT ecosystems.

- Knowledge of AI safety, Responsible AI, or model risk management frameworks.

- Experience validating multimodal or sensor-fusion models.

- Familiarity with edge accelerators and real-time operating systems (RTOS).

- Technical leadership in defining AI validation strategy and best practices.

- Strong cross-functional collaboration with firmware, product, and QA teams.

Education & Experience :

- Bachelors or Masters degree in Computer Science, Electrical Engineering, AI, or related field.

- 6-8 years of experience in AI/ML, embedded systems, or connectivity testing.