Senior Machine Learning Engineer Intelligent Operations

As a Machine Learning Engineer, you will play a key role in transforming Maya’s customer support through the use of Data Science and Machine Learning, collaborating closely with engineers to build efficient, personalized, and high-impact solutions.

NATURE OF WORK

This role focuses on building and operating machine learning and LLM-based solutions, primarily including but not limited to supporting Maya’s customer experience channels. The Machine Learning Engineer will work closely with data science, engineering, and platform teams to deliver scalable, cost-efficient AI solutions with measurable impact.

• Develop, deploy, and maintain LLM-based applications, including voice-based and text-based automated agents.
• Collaborate with data science and engineering teams to operationalize AI solutions for Maya’s customer support ecosystem.
• Identify inefficiencies and pain points in internal cloud and ML services and drive cross-functional initiatives to address them.
• Design and evaluate ML infrastructure that scales existing solutions while optimizing performance and cost.
• Analyze monitoring, logging, and performance metrics to inform system improvements and service enhancements.
• Measure solution impact and iterate based on customer satisfaction, usage, and business outcomes.
• Promote data-driven decision-making through clear, effective communication with technical and non-technical peers and stakeholders.

REQUIRED QUALIFICATIONS

• Education: Master’s degree preferred, or Bachelor’s degree in Computer Science, Statistics, or a related field.
• Experience: 6+ years in ML/AI or data science roles, with at least 3 years focused on customer-facing applications.
• Technical Expertise: Core ML, deep learning, NLP, and LLM knowledge, applied to model optimization, deployment, and service improvement.
• Deployment & MLOps: Experience deploying ML models as production-ready APIs (FastAPI, Flask) and knowledge of RPC, MCP, and cloud platforms (AWS, Databricks; exposure to GCP, Azure, Snowflake is a plus).
• Programming & Tools: Proficient in Python for developing and maintaining ML services, awareness with ML libraries (TensorFlow, PyTorch, spaCy) but largely focused on supporting tools for service integration and deployment (e.g., Pydantic, Boto3).
• Version Control & Pipelines: Skilled in Git and GitHub/GitLab; familiar with CI/CD.
• Analytical Skills: Strong problem-solving and statistical analysis capabilities to drive data-driven decisions.
• Collaboration & Communication: Exceptional ability to communicate complex technical concepts, mentor peers, and work cross-functionally to deliver impactful AI solutions.