Senior Machine Learning Engineer

TL;DR

Senior Machine Learning Engineer: Designing end-to-end ML solutions that address client needs, considering factors such as data acquisition, preprocessing, feature engineering, model selection, and deployment with an accent on architecting scalable and reliable ML systems that can handle large volumes of data and real-time processing. Focus on conducting research and experimentation to explore new ML algorithms, techniques, and frameworks that can enhance the company's offerings.

Location: Remote

Company

hirify.global is an international team.

What you will do

• Engage with clients to understand their business requirements and provide expert advice on leveraging ML and AI technologies to solve their problems
• Design end-to-end ML solutions that address client needs, considering factors such as data acquisition, preprocessing, feature engineering, model selection, and deployment
• Architect scalable and reliable ML systems that can handle large volumes of data and real-time processing.
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts to deliver successful ML projects
• Conduct research and experimentation to explore new ML algorithms, techniques, and frameworks that can enhance the company's offerings
• Present findings and results to internal teams and external stakeholders in a clear and concise manner

Requirements

• 3+ years of experience as an ML Engineer or Data Scientist, either in academia or industry
• Proficient in Python programming and experience with Python data science frameworks.
• Familiarity with common ML frameworks (e.g., PyTorch, Keras) and libraries (e.g., NumPy, scikit-learn)
• Experience with LLM agents including tool using and reasoning, for instance, the combination of RAG solution and code interpreter
• Experience with LLM fine tuning
• Upper-Intermediate or higher level of English proficiency

Nice to have

• Experience with designing complex multi-model and multi-modal ML applications and products
• Solid foundation in development of data analytics systems, including data exploration/crawling, feature engineering, model building, performance evaluation, and online deployment of models
• Experience with cloud-based tools and technologies for data pipelining, model development, and deployment, particularly AWS (Amazon Web Services)
• Familiarity with AI/ML operational tools such as Airflow, MLFlow, H2O, etc.
• Experience with MLOps tools and frameworks like Jupyter Notebook, Kubernetes, Kubeflow, Spark, etc.

Culture & Benefits

• Competitive compensation
• Remote or office work
• Flexible working hours
• Healthcare benefits: medical insurance and paid sick leave
• Continuous education, mentoring, and professional development programs
• Certifications paid by the company