Senior Machine Learning Engineer

Location: hirify.global is an international team: we have colleagues who work not only from office but also remotely from all over the world.

Overview

As a Senior Machine Learning Engineer, you will engage with clients to understand their business requirements and provide expert advice on leveraging ML and AI technologies. You'll design end-to-end ML solutions, architect scalable ML systems, and collaborate with cross-functional teams to deliver successful ML projects. The role involves staying updated with the latest advancements in ML and AI, conducting research, and presenting findings to stakeholders.

What you will do

• Engage with clients to understand their business requirements and provide advice on leveraging ML and AI technologies.
• Design end-to-end ML solutions considering data acquisition, preprocessing, and model deployment.
• Architect scalable ML systems that handle large data volumes and real-time processing.
• Collaborate with cross-functional teams to deliver successful ML projects.
• Conduct research and experimentation to explore new ML algorithms and techniques.
• Present findings to internal teams and external stakeholders.

Requirements

• 3+ years of experience as an ML Engineer or Data Scientist.
• 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, RAG solutions, and code interpreters.
• Experience with LLM fine tuning.
• Solid knowledge of machine learning and deep learning fundamentals.
• Experience with transformer-based language models.
• Hands-on experience with relational SQL and NoSQL databases.
• Upper-Intermediate or higher level of English proficiency.
• Ability to work with external clients and strong communication skills.
• Ability to mentor team members.

Nice to have

• Experience with designing complex multi-model and multi-modal ML applications and products.
• Solid foundation in development of data analytics systems.
• Experience with cloud-based tools and technologies for data pipelining, model development, and deployment, particularly AWS.
• 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.
• Domain knowledge of biology and/or chemistry.

Culture & Benefits

• Competitive compensation.
• Remote or office work options.
• Flexible working hours.
• Healthcare benefits: medical insurance and paid sick leave.
• Continuous education, mentoring, and professional development programs.
• A team with excellent tech expertise.
• Certifications paid by the company.