Senior Machine Learning Engineer

Must Haves:

• Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack: Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure (ML Studio, AI Foundry), Databricks.
• Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tuning, drift, retraining, performance optimization and production model troubleshooting.

Location: Hybrid – 3 days a week onsite

Public Sector Experience: preferred but not required

Description

The Senior Machine Learning Engineer will deploy, integrate, monitor, and operationalize AI and machine-learning solutions using modern MLOps practices. The role will establish reusable deployment patterns, tooling, monitoring, and automation that enable AI solutions to move reliably from development into production and scale across the enterprise.

Responsibilities:

• Creates machine learning models and utilizes data to train models.
• Focuses on analyzing data to find relations between the input and the desired output.
• Understands business objectives and develops models that help achieve them, along with metrics to track their progress.
• Designs and develops machine learning and deep learning systems.
• Runs machine learning tests and experiments.
• Implements appropriate machine learning algorithms.

General Skills:

• Experience managing available resources such as hardware, data, and personnel so that deadlines are met.
• Experience analyzing the machine learning algorithms that could be used to solve a given problem and ranking them by their success probability.
• Experience exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world.
• Experience verifying data quality, and/or ensuring it via data cleaning.
• Experience supervising the data acquisition process if more data is needed.
• Experience finding available datasets online that could be used for training.
• Experience defining validation strategies.
• Experience defining the preprocessing or feature engineering to be done on a given dataset.
• Background in statistics and computer programming.

A team player with a track record for meeting deadlines, managing competing priorities and client relationship management experience.