Performance engineer

3 days ago


Bangalore, India Sustainability Economics.ai Full time

Sustainability Economics.ai is a global organization, pioneering the convergence of clean energy and AI, enabling profitable energy transitions while powering end-to-end AI infrastructure. By integrating AI-driven cloud solutions with sustainable energy, we create scalable, intelligent ecosystems that drive efficiency, innovation, and long-term impact across industries. Guided by exceptional leaders and visionaries with decades of expertise in finance, policy, technology, and innovation, we are committed to making long-term efforts to fulfil this vision through our technical innovation, client services, expertise, and capability expansion.   We are seeking a highly skilled and innovative  Inference Optimization (LLM and Runtime) to design, develop, and optimize cutting-edge AI systems that power intelligent, scalable, and agent-driven workflows. This role blends the frontier of generative AI research with robust engineering, requiring expertise in machine learning, deep learning, and large language models (LLMs) and latest trends going on in the industry. to enhance model efficiency, throughput, and inference performance.   ~ Optimize  runtime performance  of inference stacks using frameworks like vLLM, TensorRT-LLM, DeepSpeed-Inference, and Hugging Face Accelerate.   ~ Design and implement  scalable model-serving architectures  on GPU clusters and cloud infrastructure (AWS, GCP, or Azure).   ~ Evaluate  hardware–software co-optimization strategies  across GPUs (NVIDIA A100/H100), TPUs, or custom accelerators.   ~ Monitor and profile performance using tools such as  Nsight, PyTorch Profiler, and Triton Metrics  to drive continuous improvement.     Education & Experience ~ D. in  Computer Science  or a related field, with a specialization in  Deep Learning, Generative AI, or Artificial Intelligence and Machine Learning (AI/ML) .  ~2–3 years of hands-on experience in large language model (LLM) or deep learning optimization, gained through academic or industry work.  Strong analytical and mathematical reasoning ability with a focus on measurable performance gains.   Collaborative mindset, with ability to work across research, engineering, and product teams.   Build and maintain  internal libraries, wrappers, and benchmarking suites  for continuous performance evaluation.   Hands-on experience in building, optimizing machine learning or Agentic Systems   at scale.  Startup DNA → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.    


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