Inference optimization engineer(llm and runtime)

Location:  Bengaluru, Karnataka      About the Company:       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.      Role Summary:    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. The ideal candidate will collaborate with cross-functional teams to build production-ready AI solutions that address real-world business challenges while keeping our platforms at the forefront of AI innovation.      Key Tasks and Accountability:      Optimization and customization  of large-scale generative models (LLMs) for efficient inference and serving.   Apply and evaluate advanced model optimization techniques  such as quantization, pruning, distillation, tensor parallelism, caching strategies, etc., to enhance model efficiency, throughput, and inference performance.   Implement custom fine-tuning pipelines  using parameter-efficient methods (Lo RA, QLo RA, adapters etc.) to achieve task-specific goals while minimizing compute overhead.   Optimize runtime performance  of inference stacks using frameworks like v LLM, Tensor RT-LLM, Deep Speed-Inference, and Hugging Face Accelerate.   Design and implement scalable model-serving architectures  on GPU clusters and cloud infrastructure (AWS, GCP, or Azure).   Work closely with platform and infrastructure teams to reduce latency, memory footprint, and cost-per-token  during production inference.   Evaluate hardware–software co-optimization strategies  across GPUs (NVIDIA A100/H100), TPUs, or custom accelerators.   Monitor and profile performance using tools such as Nsight, Py Torch Profiler, and Triton Metrics  to drive continuous improvement.     Key Requirements:      Education & Experience     Ph. 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.     Skills      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.   Pragmatic problem-solver who values efficiency, reproducibility, and maintainable code  over theoretical exploration.   Curiosity-driven attitude — keeps up with emerging model compression and inference technologies .      What You’ll Do     Take ownership of end-to-end optimization lifecycle  — from profiling bottlenecks to delivering production-optimized LLMs.   Develop custom inference pipelines  capable of high throughput and low latency under real-world traffic.   Build and maintain internal libraries, wrappers, and benchmarking suites  for continuous performance evaluation.      What you will bring      Hands-on experience in building, optimizing machine learning or Agentic Systems  at scale.  A builder’s mindset — bias toward action, comfort with experimentation, and enthusiasm for solving complex, open-ended challenges.  Startup DNA  → bias to action, comfort with ambiguity, love for fast iteration, and flexible and growth mindset.        Why Join Us      Shape a  first-of-its-kind AI + clean energy platform .   Work with a small, mission-driven team obsessed with impact.   An aggressive growth path.   A chance to leave your mark at the intersection of  AI and sustainability .