Senior ML Engineer

Greeting From BMW Techworks!!

Experience: 5 to 12 Years

Location: Bangalore

Notice period: immediate to 60days

Join our team to industrialize and deploy sophisticated machine learning models on the edge, powering the next generation of our innovative products. We're looking for a Senior ML Engineer to drive the end-to-end lifecycle of our ML systems, from initial model development to robust, real-world deployment on resource-constrained devices.

This role involves designing, developing, and deploying highly efficient and optimized ML models, including Large Language Models (LLMs) and Small Language Models (SLMs), directly onto edge devices. You will be instrumental in building the core infrastructure and pipelines that enable our AI-driven features to function with low latency and high reliability, regardless of network connectivity.

In your daily work, you'll collaborate with a talented, international, and interdisciplinary team in an agile environment. Our state-of-the-art infrastructure will give you the tools you need to tackle complex challenges and focus on creating the software that powers the future of our technology.

What should you bring along!

• 5+ years

  of professional experience in

  Machine Learning (ML)

  engineering and development.
• Proven experience in

  deploying ML models on edge devices

  , including expertise in model optimization techniques such as quantization, pruning, and ONNX.
• Hands-on experience with

  deploying and fine-tuning LLM/SLM models

  on edge platforms.
• Deep expertise in

  MLOps practices

  to build, deploy, and monitor scalable, reliable, and reproducible ML pipelines.
• Strong proficiency in

  ML frameworks

  like PyTorch and TensorFlow, and experience with specialized edge deployment frameworks.
• Solid understanding of model architectures relevant to

  LLMs, SLMs

  , and other generative models.
• Proficiency in

  Python

  and experience with modern software engineering practices.
• Knowledge of various

  ML acceleration hardware

  , such as GPUs, NPUs,TPUs, and other specialized chips, with a focus on their use in edge deployments.
• Ability to design and implement efficient, scalable, and robust ML systems from scratch.