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Metro location and mid-level (3–6yr) AI role create moderate applicant competition.
Specialized hardware benchmarking, device driver and accelerator experience reduces transferability across industries.
Explicit 4+ years plus mandatory C++/Python and hardware benchmarking skills increase filtering stringency.
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Develop and optimize scripts for deploying and benchmarking deep learning models using Python and C/C++.
Evaluate Kinara products by applying benchmarking methods and tools relevant to vision, LLM, and VLM AI models.
Prepare detailed benchmarking and analysis reports on AI workload metrics such as latency, throughput, accuracy, and power consumption for engineering and marketing teams.
Bachelor's degree in ECE or Computer Science (BTech).
4+ years of experience in Machine Learning or related fields with AI/ML/DL model exposure.
Proficiency in Python and C/C++ for AI workload scripting and low-level performance work.
Familiarity with AI accelerators, LLM frameworks, Linux environments, and benchmarking suites like MLPerf and MLFlow.
Experienced in performance evaluation of both hardware and software systems related to AI workloads.
Comfortable working with benchmarking methods specific to large language models (e.g., MMLU, HumanEval, GSM8K).
Skilled in integrating CI/CD tools, version control (GitHub), and automation within AI model deployment and evaluation pipelines.