Senior AI Model Testing & Validation Engineer
Summary
Senior engineer (6+ yrs) who validates AI/ML models deployed on Bluetooth-connected edge and embedded devices: builds automated validation frameworks in Python, tests model accuracy/robustness under real-world BLE conditions, optimizes quantized on-device inference, and supports MLOps, documentation and Responsible AI governance. Hybrid role in Bengaluru (Hennur Road).
Senior AI Model Testing & Validation Engineer
6+ Years
Bangalore - Hybrid (Hennur Road)
Immediate Joiners
We are seeking a 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 Master’s degree in Computer Science, Electrical Engineering, AI, or related field.
- 6+ years of experience in AI/ML, embedded systems, or connectivity testing.
Sincerely,
Sonia TS - HR Recruiter
