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Build and maintain the AI platform and data lakehouse, automating operations and integrating services with APIs and cloud infrastructure using Python, Kubernetes, and MLOps tools.
Build and optimize production-grade LLM systems, integrating commercial APIs and self-hosted models, and implementing RAG pipelines and end-to-end LLM workflows.
Build and deploy production-grade LLM chatbots and RAG pipelines using commercial APIs and self-hosted open-source models, optimizing for latency, cost, and reliability.
Lead training, alignment, and optimization of large language models using RLHF, SFT, and quantization; build reward models, red-team models, and optimize inference pipelines in Python/C++/Rust.
Build and maintain AI infrastructure for model hosting, training, and serving at scale using Kubernetes, cloud platforms, and GPU orchestration.
Build and optimize OCR and handwriting-recognition models for an EdTech platform, turning student essays into structured text and improving teacher feedback workflows.
Build and optimize high-performance inference APIs and ML features for generative AI models running on custom hardware using Python, PyTorch, and C++.
Build and optimize high-performance inference APIs and tools for generative AI models using Python, PyTorch, and C++ on Cerebras’ custom hardware.
Build and operate sovereign AI infrastructure for government clients, deploying GPU clusters in air-gapped data centers and cloud (Azure/GCP) using Kubernetes, GitOps, and offline artifact pipelines.
Build and deploy AI models using Python, PyTorch/TensorFlow, and frameworks like LangChain and LlamaIndex; package models for production and collaborate on R&D.
Build and fine-tune production-grade LLM systems for banking users using Python, vLLM, and model evaluation in Jakarta.
Build and deploy LLM systems for banking, fine-tuning with LoRA and vLLM, and evaluate models via offline tests and live monitoring.
Build and deploy production AI systems and LLM workflows for banking, including credit risk and compliance, using vLLM, LoRA/QLoRA, and evaluation frameworks.
Build and deploy AI-powered full-stack apps using Angular, Docker, and LLMs; optimize models and engineer prompts to solve real client problems.
Build and fine-tune large language models for Avito’s products, optimizing training pipelines and inference speed for production-scale NLP systems.
Build and maintain AI-powered DevOps pipelines for an internal AI agent ecosystem, focusing on LLM inference, MCP integration, and AI artifact tracking in software releases.
Build and evaluate open-weight AI models for Canada’s sovereign stack: quantization evals, dataset pipelines, and inference benchmarks in a hybrid Victoria office.
Build and maintain the AI platform infrastructure, including Kubernetes, vector databases, and real-time monitoring, using Java, Azure, and AI tools like GitHub Copilot.
Builds low-level system software to optimize distributed AI training and inference across thousands of GPUs using C++, Python, and CUDA.
Build and deploy AI/ML models, focusing on language technologies like NLP and dialogue systems using Python, PyTorch, and Hugging Face frameworks.
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