Senior Machine Learning Engineer

Senior Machine Learning Engineer

Requirements

• Strong software engineering foundations, with expertise in Python and experience developing production-quality systems using best practices in testing, modularity, and documentation.
• Deep experience in MLOps and ML infrastructure, including model deployment, serving, CI/CD pipelines, containerization and orchestration.
• Experience deploying AI/ML models in production, working with large-scale datasets, streaming data, or real-time inference systems.
• Hands-on experience with cloud-native data and compute platforms (Snowflake, AWS/GCP/Azure), including resource optimization and cost-aware design.
• Experience with machine learning or statistical modeling on large datasets; pre-processing, data quality, feature engineering;
• Understanding of modern AI applications, including deploying Gen-AI/LLM-based solutions with techniques like RAG, prompt chaining, or agentic workflows.

Responsibilities

• Provide technical & thought leadership; designing and implementing advanced machine learning techniques, focusing on robust & scalable solutions. Participate in all stages of development, ideation to production.
• Lead development for ML systems: Design, build, and maintain production-grade ML systems, with a focus on performance, scalability, and maintainability.
• Operationalize ML models: Partner with ML Scientists to translate models into efficient, reliable pipelines and services, enabling seamless deployment and monitoring in production environments.
• Architect end-to-end ML infrastructure: Own the full lifecycle of ML solutions — from feature engineering and data pipelines to model serving, CI/CD, observability, and retraining.
• Develop hands-on; analyze large amounts of data, manage data quality, design & develop complex ML models (and the ensuing ML solutions) including ML pipelines, deploy & manage production-grade applications end-to-end, and tell the story in a compelling manner.
• Collaborate across teams: Work closely with data scientists, data engineers, platform teams, and business stakeholders to deliver solutions that align with product and business needs.
• Champion MLOps best practices: Establish & maintain infrastructure/tooling for versioning, experimentation, testing, deployment, and monitoring of ML models.
• Enable reproducibility and scale: Develop reusable components, templates, and automation to scale ML development across use cases and teams.
• Mentor and guide: Provide technical mentorship to junior engineers and scientists on engineering practices and production workflows.

Preferred Qualifications

• High levels of curiosity, eager enthusiasm & demonstrable experience working on open-ended problems.
• A bias toward action and ownership, with the ability to take vague requirements and turn them into high-quality, scalable engineering solutions. Adaptability, to respond to fast-evolving project scope, adjusting strategies & plans accordingly; comfortable being scrappy.
• Excellent collaboration and communication skills, able to work across disciplines and present complex ideas to both technical and non-technical audiences.
• Demonstrate keen senses of ownership, collaboration and mentorship; with the ability to inspire the team & lead a project to completion.