Openings For AIML

Tekskills Inc.; Hyderabad; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Tekskills Inc. is hiring an Embedded AIML Engineer to work on AI/ML model deployment on resource-constrained devices, primarily MCUs and DSPs. The role requires deep knowledge of embedded frameworks and toolchains, with strong C/C++ and Python skills.

You will train, optimize, and evaluate models, profile performance metrics, and collaborate with hardware and software teams to deliver efficient AI solutions.

Qualifications

• Must possess AI/ML concepts and ability to train, infer, and evaluate AI models.
• Familiarity with model optimization and compression methods.
• Experience with embedded AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
• Proficient in Python and C/C++ with debugging and optimization skills.
• Experience with IDEs (Keil MDK, IAR, VS Code, Eclipse) and vendor toolchains.
• Strong understanding of MCU/DSP architectures, ARM Cortex-M/A, and memory architectures.
• Experience deploying AI on MCUs, DSPs, and edge processors; profiling performance.
• Version control (Git) and build systems (CMake, Makefiles).
• Strong verbal and written communication skills.

Responsibilities

• Develop and optimize embedded AI models for resource-constrained devices.
• Train, evaluate, and deploy AI models on MCUs/DSPs and edge processors.
• Profile latency, throughput, memory footprint, and power consumption.
• Collaborate with cross-functional teams and document results.

Skills

AI/ML concepts

Model training & inference

Model optimization

Embedded AI frameworks

IDE experience

MCU/DSP architectures

ARM Cortex-M/A

FreeRTOS/Zephyr

Python & C/C++

Git/GitHub/GitLab

Build systems (CMake/Makefiles)

Communication skills

Tools

Keil MDK

IAR Embedded Workbench

VS Code

Eclipse

ST toolchains

NXP toolchains

Renesas toolchains

TI toolchains

Infineon toolchains

CMSIS-NN

microTVM

TFLite

Job description

Hi all

We are hiring for Embedded AIML Engineer

Work Location: Bangalore

Work mode: 5 days of WFO

Experience Range:4+ years

JD:

Embedded-AI engineer must have most of the below mentioned skillsets.

1. 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).
2. Different model optimization and compression methods must be known.
3. Embedded-AI frameworks like TensorFlow, TFLite, TFLM, microTVM, CMSIS-NN, CMSIS-DSP.
4. Experience of using IDEs like: Keil MDK, IAR Embedded Workbench, VS Code, Eclipse, and toolchains from ST, NXP, Renesas, TI, Infineon etc.
5. 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.
6. 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.
7. Strong proficiency in Python and C / C++ languages. Experience with software debugging and code optimization techniques.
8. Familiarity with Git/GitHub/GitLab and build systems like CMake, Makefiles.
9. Strong verbal and written communication skills.