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Build and deploy deep-learning forecasting models for DeFi trading strategies using reinforcement learning and time-series data to maximize protocol yield.
Lead a team building enterprise generative AI and ML systems in Python, from concept to production, while defining semantic standards and ontologies to improve decision-making and automate workflows for internal customers.
Lead the design and optimization of hybrid search and ranking systems for GoFood and GoPay using lexical, dense, and generative retrieval, embeddings, and LLMs to improve relevance and personalization.
Build and scale ML systems for ad ranking, bid optimization, and real-time recommendations using PyTorch/TensorFlow to improve CTR, CVR, and ROI in a mobile advertising platform.
Sell AWS’s generative AI and ML services to enterprise customers in MENAT, crafting solutions to business problems and driving adoption of cutting-edge AI technologies.
Build and scale core ML infrastructure and systems, turning research models into production-ready services while ensuring reliability and performance for user-facing AI features.
Lead AI research and strategy to shape how the platform thinks, remembers, and acts using cutting-edge ML techniques like MoE and multimodal systems.
Build AI-powered financial analytics tools by researching new ML technologies and implementing research papers into production systems.
Lead AI/ML engineer at JPMorganChase building secure, scalable ML systems for home lending servicing, including pipelines, inference, monitoring, and MLOps while driving adoption of enterprise AI-assisted engineering practices.
Develops pre-silicon hardware/software co-design for custom ML chips, writing bare-metal software to verify SoC functionality and performance.
Design and optimize custom ASICs for AWS’s machine-learning servers, focusing on high-performance, power-efficient SystemVerilog RTL and trade-offs between features, power, and area.
Design and productionize ML models for eBay’s ranking, recommendations, personalization, and pricing systems using Python, AWS, Databricks, and Zipline.
Build and maintain AI applications for government clients using Python/Java/JavaScript, collaborating with engineers to solve data-analysis problems and deliver secure solutions.
Build and deploy production-grade AI systems, including agentic AI, RAG, and knowledge graphs, using Python and cloud-native tools on GCP.
Builds enterprise-grade Generative AI and AI/ML platform services, APIs, and agentic workflows using Python, FastAPI, Kafka, and Kubernetes to accelerate AI adoption for clients in finance and other industries.
Build and scale AI/ML systems, models, and infrastructure for a cloud communications platform, including generative AI, NLP, and MLOps pipelines.
Build and optimize ML deployment platforms and inference pipelines for autonomous-vehicle software, shipping PyTorch models to GM’s Super Cruise fleet with real-time latency and safety constraints.
Build and optimize Neuron, AWS’s ML compiler/runtime for training GenAI models on Trainium chips, tuning parallelism and kernels across PyTorch/JAX to maximize throughput.
Build and fine-tune generative AI models and agentic systems to improve AWS services and customer experiences using deep learning and reinforcement learning.
Build and deploy ML models and data pipelines in Python for a federal client, using TensorFlow, Scikit-learn, Spark, Kubernetes, and Postgres in a hybrid role near Washington, DC.
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