Machine Learning Engineer - Lead

November 16, 2023

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MUTT DATA

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machine learning • data science • deep learning • artificial intelligence • consulting

51 - 200

Description

• Project Management: Effectively manage projects by engaging with customers to understand the scope of work. • Team Leadership: Lead teams, collaborating with Machine Learning and data engineers. • Lead ML Model Productization: Champion the productization of ML models following MLops best practices, including orchestration, testing, monitoring, and serving, to benefit our clients. • ML POC Development: Collaborate with Machine Learning Engineers to develop meaningful ML Proof of Concepts (POCs) for internal and client requirements. • ML Model Lifecycle Management: Oversee the lifecycle of Machine Learning models, optimizing them when necessary to enhance performance, latency, memory, and throughput. • Business-Technical Translation: Translate business and mathematical/statistical requirements into software implementations, making informed trade-offs between time, quality, and client-specific needs. • Research and Innovation: Explore emerging ML Engineering technologies (Data Science, Data Engineering, DevOps) and techniques to enhance our toolset, best practices, and overall business value. • Project Strategy: Participate in defining project roadmaps, timelines, and estimates for new initiatives. • Knowledge Sharing: Document and disseminate industry-leading practices in AI/ML within the organization. • Technical Interviews: Collaborate on hiring interview processes (exam reviews and technical interviews)

Requirements

• Demonstrated leadership skills and experience in client interactions. • Demonstrated work experience in roles such as Machine Learning Engineer, ML Architect or similar. • In-depth understanding of AI/ML principles, encompassing neural networks, supervised and unsupervised ML models, time series forecasting, and more. • Familiarity with Modern Data Architectures, including the implementation of Data Warehouses and Data Lakes, as well as DevOps tool/stack and methodologies (CI/CD, Kubernetes, Docker, gitops, etc.). • Previous involvement with data processing ETL and ML workflows, e.g., Airflow, MLflow, DBT. • Understanding of Deep Learning frameworks and technologies such as Keras, PyTorch, Tensorflow. • Strong grasp of Python programming language and proficiency in at least one other strongly typed language. • Knowledge of mathematical modeling and proficient statistical intuition. • Experience in implementing Machine Learning-based systems, including ML model lifecycle management, monitoring, and setting up MLOps pipelines from scratch. • Ability to develop implementation plans by weighing the pros and cons of different alternatives. • Solid command of the English language for writing technical documents, such as Design Documents. Nice to have skills: • Experience with the Modern Data Stack. • Hands-on experience with cloud-based AI services like AWS Sagemaker, AWS Textract, GCP Vertex AI, or similar. • Profound knowledge of software development methodologies. • A positive problem-solving attitude. • Previous experience in client-facing tech consultancy roles. • A track record of delivering high-quality solutions. • Certification in cloud platforms, such as AWS Machine Learning. • Familiarity with Python Data libraries like SQLAlchemy, Pandas, Polars, PySpark, Great Expectations, etc.

Benefits

• Participate in compensated social events • Access quality coworking spaces • Get an additional week of vacation each year (Mutt Week) • We cover the costs of your Amazon Web Services (AWS) and Google Cloud Platform (GCP) certification exams and study materials • Enjoy a day off to celebrate your birthday • Improve your English language skills through in-company English lessons