Senior Applied Machine Learning Engineer

Responsibilities

• Design, develop, and deploy machine learning models and AI solutions that address real‑world customer problems, focusing on usability, scalability, and performance.
• Rapidly develop demos, POCs, MVPs, and workflows to showcase new AI/ML capabilities that could be integrated into the product or used to improve existing features based on customer feedback or market research.
• Develop and improve integrations of open‑source ML/AI tools (MLFlow, Spark, Lang Chain, Kubeflow) within production environments, ensuring seamless operation on platforms like Kubernetes.
• Fine‑tune models and algorithms for accuracy, efficiency, and scalability in production settings, including deep‑learning technologies.
• Translate customer requirements and industry trends into actionable AI/ML solutions that improve product features, data management, and system performance.
• Work closely with product managers, data scientists, and engineering teams to brainstorm, design, and deploy AI/ML solutions, documenting procedures and best practices.
• Lead efforts in integrating emerging AI tools, mentor junior team members, and communicate progress and challenges to leadership.

• PhD with at least 2 years of relevant industry experience, or the equivalent (Master’s degree with 4+ years, Bachelor’s with 6+ years).
• Extensive hands‑on experience applying machine learning and AI solutions in customer‑facing or end‑user environments.
• Proven ability to deploy models in production, ensuring reliability and performance.
• Experience with open‑source ML/AI tools and frameworks.
• Experience with backend programming languages (Python, Go).
• Proficiency in developing, using, and maintaining AI agents; proven experience coding agents for automation or decision‑making tasks.
• Excellent written and verbal communication skills, especially in asynchronous collaboration.

Demonstrates expertise in designing and deploying machine learning models and AI solutions, with a strong focus on usability, scalability, and performance. Proficient in integrating open‑source ML/AI tools and collaborating with cross‑functional teams to translate customer needs into actionable solutions.

• MLFlow
• Spark
• Lang Chain
• Kubeflow
• Kubernetes