Job Description :

Job Summary :

We are seeking an experienced AI Validation Engineer to lead validation, benchmarking, and quality assurance activities for AI/ML software stacks running on embedded and heterogeneous computing platforms. The ideal candidate will possess strong expertise in AI frameworks, ROCm ecosystems, Linux-based environments, performance analysis, and automation. This role will drive end-to-end AI pipeline validation while collaborating closely with architecture, compiler, runtime, driver, and hardware teams to ensure production-quality AI solutions.

Key Responsibilities :

AI/ML Validation & Quality Ownership :

- Lead validation efforts for complex AI/ML compute stacks across multiple hardware and software platforms.

- Define validation strategies, test plans, methodologies, and quality metrics for AI software and system pipelines.

- Own the complete defect lifecycle, including issue reporting, triage, root-cause analysis, tracking, and closure.

- Ensure comprehensive coverage across functional, performance, regression, stress, scalability, and reliability testing.

End-to-End AI Pipeline Validation :

- Validate complete AI workflows across training, optimization, and inference pipelines.

- Validate ROCm libraries and AI software stack functionality.

- Verify :

1. Model training, conversion, and optimization workflows (e.g., PyTorch to ONNX)

2. Inference runtimes such as ONNX Runtime, TensorRT, ROCm/HIP, and OpenVINO

3. AI compilers and toolchains including TVM, Vitis AI, XDNA, and XLA

4. Kernel execution, memory movement, inference correctness, and accuracy

- Validate AI workload stability, performance, and correctness on Ubuntu and Yocto-based Linux platforms.

AI Benchmarking, Profiling & Performance Optimization :

- Define and execute benchmarking strategies for AI training and inference workloads.

- Profile AI models to identify compute, memory, throughput, and latency bottlenecks.

- Collaborate with compiler, runtime, and hardware teams to drive system-level and model-level optimizations.

- Validate performance improvements across :

1. Model architectures

2. Batch sizes

3. Precision modes (FP32, FP16, INT8)

4. Execution paths and hardware configurations

- Ensure performance regressions are detected early and release performance targets are consistently achieved.

AI Framework & Compute Stack Validation :

- Validate functionality, integration, and performance of AI frameworks including :

1. PyTorch

2. TensorFlow

3. ONNX Runtime

- Execute and validate workloads across heterogeneous compute environments utilizing :

1. ROCm/HIP

2. CUDA

3. OpenCL

4. AI accelerators

- Analyze the impact of framework, compiler, and runtime changes on real-world AI workloads.

Automation & Tool Development :

- Design and develop Python-based validation, benchmarking, and profiling frameworks.

- Build reusable automation for :

1. Test execution

2. Benchmarking

3. Performance profiling

4. Result analysis

5. Reporting and dashboards

- Continuously improve validation efficiency, scalability, and coverage through automation.

Technical Leadership :

- Provide technical leadership and mentorship to validation engineers and junior team members.

- Partner with architecture, compiler, runtime, driver, and hardware teams to resolve functional and performance issues.

- Collaborate effectively with globally distributed cross-functional teams.

- Present validation status, benchmarking results, quality metrics, and performance risks to stakeholders.

Required Skills & Qualifications :

Technical Expertise :

- 8-12 years of experience in AI/ML validation, performance analysis, or software quality engineering.

- Strong understanding of :

1. Deep Learning

2. Large Language Models (LLMs)

3. Recommender Systems

- Strong hands-on experience with ROCm technologies and ROCm stack validation.

- Experience validating AI/ML compute stacks including :

1. HIP

2. CUDA

3. OpenCL

4. OpenVINO

5. PyTorch and TensorFlow ecosystems

- Expertise in end-to-end AI pipeline validation including :

1. Model conversion

2. Inference runtimes

3. AI compilers

4. Kernel execution

5. Accuracy validation

- Advanced Python programming and scripting skills.

- Strong experience in AI benchmarking, profiling, and performance optimization.

- Deep understanding of Linux environments, particularly Ubuntu and Yocto.

Validation & Quality Engineering :

- Strong experience with software validation methodologies, SDLC processes, and defect management.

- Experience with production-quality software validation and release qualification.

- Strong focus on reproducibility, test coverage, performance validation, and release readiness.

- Ability to independently drive validation initiatives with strong ownership and accountability.

Preferred Qualifications :

- Experience benchmarking and optimizing AI workloads on heterogeneous platforms including CPUs, GPUs, and AI accelerators.

- Experience tuning large-scale AI models, including :

1. Memory optimization

2. Mixed-precision execution

3. Inference acceleration

- Familiarity with open-source AI ecosystems and performance-focused projects.

- Exposure to embedded AI platforms and edge AI deployments.

Desired Attributes :

- Strong analytical and performance-focused problem-solving mindset.

- Excellent communication and stakeholder management skills.

- Proven ability to lead technically complex validation programs.

- Ability to work effectively in fast-paced, globally distributed engineering environments.

Educational Qualifications :

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electronics Engineering, Robotics, or a related field.

Supplier Notes :

- Strong experience in AI Validation, ROCm Validation, and AI Performance Benchmarking is mandatory.

- Candidates must have hands-on expertise with PyTorch, TensorFlow, ONNX Runtime, ROCm/HIP, and Linux (Ubuntu/Yocto) environments.

- Preference will be given to candidates with experience in LLMs, AI accelerators, heterogeneous compute platforms, and performance optimization.

- Strong Python automation and validation framework development experience is required.