Futuralis
11 - 50 employees
Founded 2011
🔒 Cybersecurity
Cloud Services • AI/ML • Cybersecurity
Futuralis is a cloud services and consulting company that empowers businesses to enhance their operations through advanced technologies like AWS Cloud Services, application modernization, and AI/ML solutions. They offer a range of services including cloud assessment, data analytics, application development, and security assessments to help organizations optimize costs, improve system performance, and safeguard against cybersecurity threats. With a focus on innovation and efficiency, Futuralis aims to elevate businesses into the future of digital transformation.
ML Data Scientist
Job not on LinkedIn
🕒 February 23
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Futuralis
11 - 50 employees
Founded 2011
🔒 Cybersecurity
Cloud Services • AI/ML • Cybersecurity
Futuralis is a cloud services and consulting company that empowers businesses to enhance their operations through advanced technologies like AWS Cloud Services, application modernization, and AI/ML solutions. They offer a range of services including cloud assessment, data analytics, application development, and security assessments to help organizations optimize costs, improve system performance, and safeguard against cybersecurity threats. With a focus on innovation and efficiency, Futuralis aims to elevate businesses into the future of digital transformation.
📋 Description
• Build and deploy end-to-end AI solutions • Develop machine learning and generative AI models • Perform data analysis • Scale solutions in production using cloud and MLOps practices
🎯 Requirements
• 5+ years of experience • Experience in design/architecture of ML solutions • Strong programming skills in Python, Java, or Scala • Solid foundation in statistics, machine learning, and model evaluation • Experience with deep learning frameworks (PyTorch, TensorFlow) and LLMs (GPT, BERT) • Hands-on experience with end-to-end ML pipelines and deployment • Knowledge of Generative AI (RAG, prompt engineering, fine-tuning) • Experience with big data tools (Spark, Kafka, SQL, ETL pipelines) • Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps practices • Experience with Docker, Kubernetes, and scalable systems
🏖️ Benefits
• Flexible work arrangements • Professional development
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