Machine Learning Engineer

Responsibilities
Own small to medium components of machine learning systems from technical designthrough implementation and delivery
Translate technical requirements into high-quality, maintainable code and deliver workstreamsaccording to plan
Build and maintain data pipelines and feature engineering workflows to support machinelearning and AI solutions
Design, train, evaluate, and refine machine learning models with minimal supervision, applyingsound statistical and engineering practices
Implement ML solutions that can be deployed into production environments as microservices,APIs, batch jobs, or streaming components
Support production monitoring efforts by helping define and implement metrics for modelperformance, data drift, anomalies, and retraining triggers
Collaborate with Data Engineers, Software Engineers, Data Scientists, Product partners, andbusiness stakeholders to deliver project objectives
Understand system design, data models, and technical artifacts well enough to contribute toimplementation decisions and tradeoffs
Follow governance, documentation, coding, and source control standards consistently
Demonstrate flexibility and proactively support teammates with day-to-day responsibilities asneeded
Clearly document and communicate work progress, technical decisions, and outcomes totechnical and non-technical audiences
Required Experience & Skills
Completed BS, MS, or PhD in Computer Science, Mathematics, Statistics, Data Science,Engineering, Operations Research, or other quantitative field
3+ years of practical experience building, evaluating, scaling, and deploying machine learning pipelines with Python
Strong programming skills in Python and solid understanding of core computer scienceprinciples
Experience with data manipulation frameworks such as Pandas and PySpark
Experience with machine learning libraries such as scikit-learn, HuggingFace,TensorFlow/Keras, PyTorch, or MLlib
Experience with MLOps practices such as automated model deployment, model performancemonitoring, data drift detection
Working knowledge of SQL and relational data structures
Ability to design, train, and evaluate machine learning models using standard best practicessuch as model selection, validation, bias/variance tradeoffs, and performance assessment
Familiarity with batch and streaming data pipeline concepts such as ETL, ELT, and stream processing
Experience working with cloud environments, preferably AWS
Familiarity with technologies such as APIs, microservices, Docker, and Kubernetes
Strong interpersonal, verbal, and written communication skills
Ability to work effectively in a remote environment using collaboration tools
Preferred Experience & Skills
Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
Experience with managing and architecting solutions on AWS
Familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
Familiarity with Snowflake, RDS, DynamoDB, Kafka, Fivetran, dbt, Airflow, Docker, Kubernetes,
EMR, Sagemaker, DataDog, PagerDuty, Data Cataloging tools, Data Observability tools and Data Governance tools

Employment type
Full-time

Work arrangement
Yes