AIML Engineer

Job highlights

• M.Tech in AI/ML with Electronics & Communication or Computer Science background, 4+ years experience with 3+ years in AI/ML, expertise in deep learning architectures (CNN, RNN, GNN, Transformers), strong Python and AI/ML framework skills (PyTorch/TensorFlow)
• Train, evaluate, and optimize AI models; deploy models on embedded/resource-constrained devices; use embedded AI frameworks and IDEs; perform performance profiling and code optimization; collaborate on AI/ML research and development

Job Summary

We are hiring for AI/ML Engineer.

Work mode: 5 days of WFO. Experience range: 4+ years. JD Embedded-AI engineer must have most of the below mentioned skillsets. Must be good at AI-ML concepts. Must be able to perform model training, inference, and evaluation of different AI models (both supervised and unsupervised). Different model optimization and compression methods must be known. Embedded-AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP. Experience of using IDEs like: Keil MDK, IAR Embedded Workbench, VS Code, Eclipse, and toolchains from ST, NXP, Renesas, TI, Infineon etc. Strong understanding of MCU and DSP architectures. Knowledge of ARM Cortex-M/A processors and embedded memory architectures. Experience with FreeRTOS and Zephyr is an added advantage. Experience in porting and deploying AI models on resource-constrained devices such as MCUs, DSPs, and edge processors. Performance profiling and optimization of Latency, Throughput, Memory footprint, and Power consumption. Strong proficiency in Python and C / C++ languages. Experience with software debugging and code optimization techniques. Familiarity with Git/GitHub/GitLab and build systems like CMake, Makefiles. Strong verbal and written communication skills.

Role Overview

We are seeking an experienced AI/ML Engineer with a strong academic and research foundation to contribute to the design, development, and implementation of advanced machine learning and deep learning solutions. The role requires expertise in state-of-the-art architectures, model optimization, and problem-solving applied to real-world scenarios.

Responsibilities

• Perform model training, inference, and evaluation of different AI models (both supervised and unsupervised).
• Know various model optimization and compression methods.
• Embedded-AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
• Experience using IDEs like Keil MDK, IAR Embedded Workbench, VS Code, Eclipse, and toolchains from ST, NXP, Renesas, TI, Infineon etc.
• Strong understanding of MCU and DSP architectures; ARM Cortex-M/A processors and embedded memory architectures.
• Experience with FreeRTOS and Zephyr (added advantage).
• Experience in porting and deploying AI models on resource-constrained devices such as MCUs, DSPs, and edge processors.
• Performance profiling and optimization of latency, throughput, memory footprint, and power consumption.
• Strong proficiency in Python and C / C++ languages.
• Experience with software debugging and code optimization techniques.
• Familiarity with Git/GitHub/GitLab and build systems like CMake, Makefiles.
• Strong verbal and written communication skills.

Qualifications

• Mandatory M. Tech in AI/ML with background in Electronics & Communication (EC) or Computer Science (CS).
• Experience: 4+ years total, with at least 3+ years relevant in AI/ML domain.

Core Skillset (Must-Have)

• Expertise in Deep Learning architectures: CNN, RNN, GNN, Transformers.
• Strong programming skills in Python.
• Experience with at least one major AI/ML framework (PyTorch / TensorFlow).
• Track record in Computer Vision, Time-Series Analysis, Video Analysis.
• Familiarity with Unsupervised & Semi-supervised learning techniques.

Preferred/Good to Have

• Image Processing, NLP, Edge/Embedded AI deployment.

Additional Notes

Design and develop cutting-edge AI/ML models for vision, time-series, and multimodal data. Research and prototype advanced algorithms using state-of-the-art methods in deep learning. Optimize models for performance and scalability across platforms. Collaborate with cross-functional teams on real-world product and research applications. Stay ahead of AI/ML trends, and apply them creatively to business challenges.