ML Engineer

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July 3

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Jimmy Technologies

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Automotive • Fintech • Transport

Jimmy Technologies is a company specializing in providing technology solutions and innovations for the mobility industry. They focus on automotive, mobility, and fintech sectors, utilizing high-skilled development teams and proven business strategies to ensure innovative outcomes and rapid market entry. Their services include end-to-end product realization, cloud services, and technical consulting, emphasizing value-centric and outcome-driven approaches. With the implementation of their Jimmy Framework and Components, they enable cost-cutting and faster product launches, offering tailored solutions from pre-built modules developed in-house. By collaborating with clients from the initial idea to product launch, Jimmy Technologies aims to boost business efficiency and achieve significant revenue gains.

11 - 50 employees

💳 Fintech

🚗 Transport

📋 Description

• Our client, a leader in data-driven solutions, is seeking ML Engineers to contribute to their AI-driven automation and efficiency projects in the US. • This role is part of a larger company’s strategy leveraging Generative AI (GenAI) to enhance workflows, decision-making, and data management of the enterprise solutions in tax, auditing and risk management used by the largest companies in the world. • The project is focused on building proof-of-concept (POC) applications and then converting them into scalable, production-ready systems using large-scale Neural Networks, Deep Learning and Reinforcement Learning techniques. • Responsibilities include developing and training transformer-based models for text and image processing, ensuring high performance and scalability, and collaborating with cross-functional teams.

🎯 Requirements

• Strong expertise in large-scale Neural Networks, Deep Learning, and Reinforcement Learning techniques, with a focus on real-world applications. • Hands-on experience with GenAI projects and related frameworks, including RAG applications, vector databases, LangChain, LlamaIndex, and agentic frameworks. • Advanced proficiency in Python and machine learning libraries, such as SciPy, Scikit-learn, TensorFlow, PyTorch, pyMC, and pgmpy. • Practical experience with cloud computing platforms, preferably Azure, but also AWS or GCP. • Deep understanding of the full ML lifecycle, with hands-on experience in MLOps and DataOps practices. • Strong background in Probabilistic Graphical Models, including Bayesian Networks, Markov Random Fields, and Factor Graphs. • Excellent problem-solving skills and keen attention to detail.