MUTT DATA is hiring a
Machine Learning Engineer Lead

Summary

Mutt Data is a remote-first startup that collaborates with tech startups and major corporations in various countries. They are looking for a Machine Learning Engineer Lead to manage projects, lead teams, develop ML Proof of Concepts (POCs), and oversee the lifecycle of Machine Learning models. The role requires team leadership, AI/ML proficiency, data architecture knowledge, ETL and ML workflow experience, deep learning competence, programming skills, mathematical modeling, strategic thinking, and intermediate English level.

Requirements

• Team Leadership and Client Interactions: Demonstrated leadership skills and experience in client interactions
• Proven Expertise: Demonstrated work experience in roles such as Machine Learning Engineer, ML Architect or similar
• AI/ML Proficiency: In-depth understanding of AI/ML principles, encompassing neural networks, supervised and unsupervised ML models, time series forecasting, and more
• Data Architecture Knowledge: 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.)
• ETL and ML Workflow Experience: Previous involvement with data processing ETL and ML workflows, e.g., Airflow, MLflow, DBT
• Deep Learning Competence: Understanding of Deep Learning frameworks and technologies such as Keras, PyTorch, Tensorflow
• Programming Skills: Strong grasp of Python programming language and proficiency in at least one other strongly typed language
• Knowledge of mathematical modeling and proficient statistical intuition
• MLOps Mastery: Experience in implementing Machine Learning-based systems, including ML model lifecycle management, monitoring, and setting up MLOps pipelines from scratch
• Strategic Thinking: Ability to develop implementation plans by weighing the pros and cons of different alternatives
• English Intermediate Level

Responsibilities

• 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)

Preferred Qualifications

• Experience with the Modern Data Stack
• Cloud-Based AI Services: Hands-on experience with cloud-based AI services like AWS Sagemaker, AWS Textract, GCP Vertex AI, or similar
• Software Development Expertise: Profound knowledge of software development methodologies
• Problem-solving attitude: A positive problem-solving attitude
• Consultancy Experience: Previous experience in client-facing tech consultancy roles
• Proven Track Record: A track record of delivering high-quality solutions
• Cloud Certifications: Certification in cloud platforms, such as AWS Machine Learning
• Python Data Libraries: Familiarity with Python Data libraries like SQLAlchemy, Pandas, Polars, PySpark, Great Expectations, etc

Benefits

• Social Paid Events
• Worknmates Coworking Spaces
• Mutt Week
• Paid AWS and GCP Certification Exams
• Birthday Free Day
• In-Company English Lessons
• Referral Bonuses
• Remote First Culture
• Annual Mutters' Day
• Annual Mutters' Trip