My client is looking for both Senior and Lead level Machine Learning Engineers to launch a new array of game-changing technologies on their successfully adopted B2C/DTC/B2B2C platform.

The organization has a startup-oriented style environment (so you will be able to wear multiple hats at any given time and be able to have a lot of say when it comes to decision making), while having the backing of a very large company (so you will also have job security).

As a Lead/Senior ML Engineer, you will be responsible for collaborating with cross functional partners and applying your Machine Learning Engineering skills to deliver data-driven solutions for product teams, operations, marketing, and sales.

Responsibilities

• Oversight and allocation of budgets and resources to ensure milestones and projects timelines are adhered to and that defined KPIs/OKRs are being met.
• Leading and managing a small team of ML engineers where you will be leading by example through setting KPIs/OKRs, supervising, removing barriers, cultivating talent through upskilling and creating a positive and collaborative team environment.
• Stakeholder management of all levels from C-suite, multi-disciplinary teams (Product, data, Software dev, Sales and marketing) Vendor/Agency management and other external stakeholders to build/refine relevant data products.
• Architect, design, train, and evaluate machine learning and AI models while adhering to best practices including model selection, validation, bias/variance tuning, performance assessment, sensitivity analysis, dimensionality reduction, etc.
• Champion code quality, reusability, scalability, maintainability, and security as well as providing input for strategic architecture decisions to implement best practice frameworks, data governance and processes - ensuring data quality consistency amongst the ML/AI and Data teams.
• Integrate Machine Learning and AI systems with production applications whilst continuing to Innovate with new approaches, staying aligned with up to date/current research and latest technologies in the broader ML engineering community

Required Experience & Skills

• BS/MS/PhD educated in one of the following fields: Computer Science, Mathematics, Statistics, Data Science, Engineering, Operations Research, or other quantitative field
• 7+ years of experience as an engineer specialized building Machine Learning systems
• 1+ years of Line management experience of (individual contributor) Machine Learning Engineers in projects to deliver data solutions
• Strong programming skills in Python and understanding of core computer science principles
• Experience with frameworks and libraries for machine learning & AI such as scikit-learn, HuggingFace, PyTorch, Tensorflow/Keras, MLlib, etc.
• Experience with MLOps practices such as automated model deployment, model performance monitoring, data drift detection, etc.
• Experience with building batch and streaming pipelines using complex SQL, PySpark, Pandas, and similar frameworks
• Experience with data warehouses (e.g., dimensional modeling), data lakes/Lakehouses, and other data architectures
• Experience with orchestrating complex workflows and data pipelines using like Airflow or similar tools
• Experience with Git, CI/CD pipelines, Docker, Kubernetes, architecting solutions on AWS or equivalent public cloud platforms
• Experience with developing data APIs, Microservices and event driven systems to integrate ML systems and familiarity with Large Language Models (LLMs), other generative AI modalities, and how they are applied in production
• Experience in assessing and implementing new data tools to enhance the machine learning stack

Preferred Experience & Skills:

• Knowledge of data mesh concepts
• Knowledge in domains such as recommender systems, fraud detection, personalization, and marketing science
• Knowledge of vector databases, knowledge graphs, and other approaches for organizing & storing information
• 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