AI/ML Engineer

An AI/ML Engineer is responsible for designing, developing, and deploying machine learning or artificial intelligence solutions that solve complex problems and enhance business outcomes. This role involves leveraging AI technologies to optimize processes, improve efficiency, and drive strategic business growth. This role will work on developing advanced models, integrating them into systems, and collaborating with cross-functional teams to deliver innovative AIdriven solutions that support the organization's objectives.

Key Responsibilities:

• Design, develop, and train machine learning and deep learning models
• Experiment with different architectures and configurations to optimize model performance
• Integrate AI models into existing systems and applications
• Deploy models into production environments and ensure their proper functioning
• Optimize models for performance, scalability, and cost-effectiveness
• Conduct research to explore new methodologies and techniques for LLM optimization
• Work closely with data engineers, business intelligence, software engineers, product managers and management
• Communicate technical concepts and results to non-technical stakeholders
• Collaborate to define project requirements and deliverables
• Monitor the performance of the models in production and continuously improve models based on feedback and new data
• Document the development process, models, and algorithms
• Prepare reports and presentations on project progress and outcomes

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field
• Proven working experience in machine learning, deep learning, and AI model development
• Hands-on experience with AI frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, v, etc)
• Proficiency in Python and one or more programming languages such as Java, Javascript, or GoLang is a plus
• Experience with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Understanding of software development principles and practices
• Strong problem-solving abilities and analytical thinking
• Ability to work collaboratively in a team environment and effectively communicate technical concepts to non-technical stakeholders

• Supportive teammates
• Health insurance benefits
• Self development activities that support careers
• Hybrid working arrangement