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Design, build, and deploy AI/ML models and services, including generative AI and RAG systems, while ensuring responsible AI practices and robust MLOps pipelines.
Build and maintain scalable data pipelines, vector stores, and LLM-driven BI systems using Python, Spark, and cloud ML services to power real-time AI applications.
Build and own the MLOps and data infrastructure for a neurotechnology startup, designing pipelines to ingest, version, and serve neural data for scalable model training and deployment.
Lead a small team of senior data engineers to build and maintain scalable data infrastructure (Spark, Kafka, Airflow) and enable AI/ML workflows for a global fashion resale platform.
Builds NLP models in Python to extract and reconcile financial data from documents using OCR, fuzzy matching, and LLMs, then deploys them as REST APIs on Azure.
Build and maintain cloud and IoT platforms, CI/CD pipelines, and Azure-based environments; automate device-to-cloud data flows and support MLOps for ML model deployment.
Builds and maintains Android apps in Kotlin, integrating AI features and cross-platform frameworks like KMP/Flutter for Motorola Mobility devices.
Build and scale cloud infrastructure and CI/CD pipelines for a psychological science startup integrating generative AI and ML, ensuring reliability, security, and rapid product delivery.
Build and scale cloud infrastructure and CI/CD pipelines for a behavioral-science AI platform, deploying ML models and ensuring reliability, security, and observability across AWS/GCP/Azure.
Design, build, and deploy enterprise-scale AI/ML solutions using Python, SQL, and frameworks like TensorFlow/PyTorch, with cloud platforms (Azure/AWS/GCP) and MLOps practices.
Principal Data Engineer designs and delivers secure, scalable data pipelines and cloud solutions for defence and government programmes, mentoring teams and deploying ML models.
Build and maintain cloud-native MLOps and data pipelines for a biotech firm, deploying and scaling ML models in AWS/GCP while ensuring reliability, security, and reproducibility.
Senior MLOps & Data Engineer builds and maintains cloud-based AI infrastructure on AWS/GCP, deploys ML models, and creates data pipelines for a biotech company.
Design, build, and deploy enterprise-scale AI/ML solutions using Python, SQL, and frameworks like TensorFlow/PyTorch across Azure, AWS, or GCP.
Lead a team building and deploying enterprise-scale ML pipelines and data platforms for JPMorgan Chase, using AWS, PySpark, TensorFlow, and responsible AI practices.
Build and maintain a bioinformatic web platform and cloud infrastructure for a biopharma company, using Python, JavaScript (Svelte), PostgreSQL, Docker, GCP, and CI/CD pipelines.
Build and deploy ML models for healthcare analytics, refactoring data scientists' models into production batch jobs and optimizing cloud costs on AWS.
Build 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 banking and consumer sectors.
Lead a team building AI-powered fraud detection, digital identity, and behavioural intelligence services using Python and Java, setting technical direction and modern MLOps practices.
Lead a team building AI-powered risk solutions, deploying ML services, and shaping platform architecture for fraud, identity, and credit risk products.
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