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Builds and deploys Python-based ML services and APIs for fintech, logistics, and healthcare clients using Django/Flask, TensorFlow, and DevOps/MLOps tooling.
Build and deploy NLP models and LLM-powered backends using Python, FastAPI, PyTorch/TensorFlow, and cloud tools like Google Cloud and Airflow.
Build and maintain enterprise back-end systems in Python using frameworks like Django REST or FastAPI, collaborating with cross-functional teams to deliver scalable digital solutions.
Build and maintain insurance-focused applications using Python, AI/ML models, and AWS cloud services, integrating systems via APIs and microservices.
Build and maintain full-stack applications using Python, .NET, FastAPI, and Next.js/React, with a focus on AI/ML integration, cloud architecture, and REST APIs.
Build and deploy AI/ML models in Python for fintech use cases, using LLMs and MLOps pipelines while leveraging AI tools to accelerate development.
Builds and maintains Python-based generative AI systems for a large financial services firm, writing clean, scalable code and collaborating in an Agile team to deliver features and meet sprint goals.
Build NLP pipelines with Flair/BERT/LLMs, process large datasets in PySpark/Pandas, and deploy ML models via Flask APIs and MLflow for Citi’s fintech and AI products.
Build and deploy generative-AI features in Python for a large bank’s internal tools and customer-facing systems, using frameworks like FastAPI, TensorFlow, and cloud-native stacks.
Develop and deploy GenAI solutions in Python, focusing on RAG, fine-tuning, and multi-model pipelines, while benchmarking LLM performance and building scalable APIs for production use.
Build and deploy GenAI solutions in Python, integrating LLMs with RAG, fine-tuning, and multi-agent pipelines while maintaining robust APIs and benchmarking model performance.
Build and maintain scalable ML pipelines on Azure, automating training, deployment, and monitoring with DevOps practices and Kubernetes.
Build, test, and deploy ML models and AI applications using Python, PyTorch, TensorFlow, and data tools like Pandas and NumPy.
Build and shape an AI-powered product from concept to production, designing backend services, APIs, and cloud infrastructure while integrating machine learning and computer vision.
Build and optimize production-grade face biometrics and identity-verification systems using deep learning and computer vision, focusing on liveness detection, anti-spoofing, and fraud prevention.
Build and tune LLM-based AI agents and RAG systems for ERP automation and SaaS knowledge bases, using Python, PyTorch, LangChain, and cloud pipelines.
Lead the design, build, and deployment of production-grade AI systems for high-stakes clients, focusing on scalable ML infrastructure and responsible AI practices.
Build and deploy production-grade machine learning systems for government and enterprise clients, using Python, cloud platforms, and ML frameworks like TensorFlow or PyTorch.
Build and deploy production-grade AI systems for government and enterprise clients, focusing on scalable ML architecture and responsible AI deployment.
Build and deploy ML models to detect abuse and protect Apple’s ecosystem, using LLMs and deep learning while ensuring user privacy and security.
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