AI/ML Engineer Offline

We are looking for Junior/Middle AI Engineer to join our team. 

What you will be taking part in

• Build and iterate on commercial MVPs, transforming early ideas and research into working AI-powered solutions
• Validate and verify product ideas through experiments, prototypes, and data-driven evaluation
• Contribute to the development of internal platforms and customer-facing products, collaborating closely with senior engineers and researchers
• Design and implement end-to-end pipelines, covering data ingestion, feature processing, model training, evaluation, deployment, and monitoring
• Apply Data Engineering and MLOps best practices to ensure solutions are reliable, scalable, and maintainable
• Work with cloud-based infrastructure to deploy, run, and operate AI systems in production environments
• Engage with a wide range of business domains, adapting AI solutions to different use cases and client needs

Responsibilities

• Work with senior members and proactively communicate progress and results on assigned tasks
• Ensure a clear understanding of what needs to be delivered and the expected results
• Continuously develop skills and knowledge within AI/ML field
• Work at the intersection of AI engineering, data engineering, data science, and day-to-day MLOps
• Produce clear, well-structured artifacts of your work and research, including code, documentation, and plans
• Participate in research and development across internal company products, commercial products, and client integration projects

• Be ready to communicate in English with external teams, clients and partners

   

Requirements

• Strong proficiency in Python, with experience writing clean, maintainable, and well-structured code
• Hands-on experience with PyTorch, including building, training, and evaluating neural network models
• Educational or practical background in Machine Learning, Data Science, Artificial Intelligence, or a related field
• Solid understanding of core ML concepts such as supervised and unsupervised learning, model evaluation, overfitting, and optimization
• General understanding of transformer architectures and large language models (LLMs), including how they are trained, fine-tuned, and used in downstream applications
• Familiarity with model evaluation and efficiency metrics, such as accuracy, precision/recall, latency, memory usage, throughput, and cost-related tradeoffs
• Ability to reason about model performance vs. resource constraints, especially in production or MVP environments
• Experience contributing to backend or frontend development tasks, such as integrating ML models into applications, building APIs, or working with basic UI components

• Basic understanding of software engineering best practices, including version control (Git), code reviews, and collaborative development

   

Nice-to-Have skills:

• Experience with agentic architectures and principles 
• Experience with Databricks, AWS, Azure.