Machine Learning Engineer

Responsibilities:
We are looking for seasoned Machine Learning Engineer to work with our existing team of Data Scientists and Engineers to use AI/ML technology in supporting Federal use cases. We are looking for a more than just a "Machine Learning Engineer", but a technologist with excellent communication and customer service skills and a passion for data and problem solving.

• Collaborate with cross-functional teams to design and develop machine learning models and algorithms to address business needs.
• Analyze and process large datasets to extract meaningful insights and patterns.
• Collaborate with cross-functional teams to integrate machine learning solutions into existing systems and workflows.
• Optimize and fine-tune machine learning models for performance, scalability, and accuracy.
• Implement and maintain data pipelines to support machine learning workflows.
• Evaluate the effectiveness of machine learning models using appropriate metrics and validation techniques.
• Document processes, experiments, and results to ensure reproducibility and knowledge sharing.
• Deploy machine learning models to production environments and monitor their performance.
• Stay updated with the latest research and trends in AI to implement cutting-edge solutions.
• Support an Agile software development lifecycle
• You will contribute to the growth of our AI & Data Exploitation Practice!

Qualifications:

• Master's degree and 3 years of relavent experience; OR
• Bachelor's degree and 5 years of relavent experience; OR
• No degree and 9 years of relavent experience

Possesses at least one professional certification relevant to the technical service provided. Maintain a certification relevant to the product being deployed and/or maintained.

Ability to hold a position of public trust with the US government.

2-4 years industry experience developing ML/AI solutions and a passion for solving complex problems.

2-4 years industry experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn.

Strong programming skills in languages such as Python.

Solid understanding of statistical methods, data structures, and algorithms.

Experience with data preprocessing, feature engineering, and model evaluation techniques.

Familiarity with big data technologies like Spark.

Knowledge of cloud platforms such as AWS, Google Cloud, or Azure.

Experience with version control systems, such as Git, and continuous integration/continuous deployment (CI/CD) practices.

Excellent problem-solving skills and attention to detail.

Strong communication skills and ability to work collaboratively in a team environment.

Ability to manage multiple tasks and projects simultaneously while meeting deadlines.