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Openings For Embedded AIML Engineer

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Onsite

Openings For Embedded AIML Engineer

Tekskills

Openings For Embedded AIML Engineer

Skills

Deep LearningPythonPyTorchTensorFlowGitLabMachine LearningAIGitHubTalent AcquisitionNLP / Natural Language Processing

1 month ago

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Important information


Contract type:

Permanent

Location:

Bengaluru, Karnataka, India

Work mode:

Onsite

Published on:

8 September 2026

What they need


Hi

We are hiring for Embedded AIML Engineer

Experience: 4+ years

Location: Bangalore

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.

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

Roles & Responsibilities:

  • 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.
  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.

Strong verbal and written communication skills

Thanks & Regards

Swathi.P

IT Recruiter - Talent Acquisition Group

+91 040-67284903/8919969275 | [email protected]

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