ML Engineer

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.

This is a remote-first position for engineers based in Europe, Turkey, Georgia, Armenia, or the United Arab Emirates with a required overlap of US working hours (2-7 PM CET).

Responsibilities

• Develop and train transformer-based models for text and image processing, ensuring high performance and scalability.

• Design and manage the full model lifecycle, from data preparation and model architecture to training, validation, deployment, and continuous monitoring.

• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to build and implement AI-driven solutions that align with business objectives.

• Stay up to date with the latest advancements in AI and machine learning, incorporating cutting-edge techniques and technologies to enhance model effectiveness.

• 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.

Work Conditions

• Start Date: ASAP

• Location: Remote (99%); must be able to travel freely within the UK & Europe for workshops.

• Onsite Requirements: Mandatory planning sessions/workshops (1x per year).

• US Time Zone Overlap: Required (2 AM - 7 PM CET)