Machine Learning Engineer - Data Engineering & Algorithm

Role Summary :

We are seeking a highly skilled and motivated ML Engineer with a strong background in data engineering and a deep understanding of algorithms to join our dynamic team. The ideal candidate will be instrumental in designing, developing, and deploying machine learning applications, focusing on building robust data pipelines and implementing cutting-edge algorithms. You will play a key role in the entire lifecycle of our ML projects, from data ingestion and feature engineering through to model deployment and performance monitoring, contributing directly to initiatives like our "QuickStrike" batch process flow.

Key Responsibilities :

- Design, build, and maintain scalable and reliable data pipelines for ingesting, processing, and transforming large datasets (as seen in our Data Ingestion phase using tools like Modin).

- Develop, train, and deploy machine learning models for various applications, including risk scoring and segmentation (aligning with our AI Core and Segmentation phases).

- Perform comprehensive feature selection, data cleaning, and preprocessing to ensure high-quality data for model development.

- Implement and optimize complex algorithms for machine learning tasks, ensuring efficiency and accuracy.

- Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to define project requirements and deliver ML-powered solutions.

- Design and implement strategies for model evaluation, outcome tracking, and continuous improvement based on feedback loops.

- Develop systems for action execution based on model outputs and rule-based strategies.

- Ensure final logging, error summary, and cleanup for batch processes.

- Stay up-to-date with the latest advancements in machine learning, data engineering, and relevant technologies.

- Document all processes, models, and pipelines effectively.

Required Skills and Qualifications :

- Bachelor's or master's degree in computer science, Engineering, Statistics, Mathematics, or a related quantitative field.

- Proven experience (2+ years) as a Machine Learning Engineer, Data Engineer, or a similar role with a focus on building ML applications.

- Strong Data Engineering Skills :

1. Proficiency in building and optimizing data pipelines using tools and frameworks such as Python (Pandas, Modin), Spark, Airflow, etc.

2. Experience with data warehousing solutions (e.g., Redshift, BigQuery, Snowflake) and database technologies (SQL and NoSQL).

3. Solid understanding of ETL/ELT processes and data modeling.

- Strong in Algorithms :

1, Deep understanding of fundamental machine learning algorithms (e.g., regression, classification, clustering, dimensionality reduction) and their mathematical underpinnings.

2. Proficiency in algorithm design, complexity analysis, and optimization.

3. Experience in implementing algorithms from scratch and using ML libraries.

- Programming Proficiency :

1. Expert-level Python programming skills.

2. Experience with ML libraries and frameworks such as Scikit-learn, TensorFlow, PyTorch, Keras.

- Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau).

- Strong analytical and problem-solving abilities.

- Excellent communication and collaboration skills.

- Ability to work independently and manage multiple tasks effectively.

Preferred Qualifications :

- Experience with MLOps practices and tools (e.g., MLflow, Kubeflow, DVC).

- Familiarity with cloud platforms such as AWS (S3, SageMaker, EMR), Azure (Blob Storage, Azure ML), or GCP (Cloud Storage, AI Platform).

- Experience in developing and deploying models in a production environment.

- Knowledge of containerization technologies like Docker and orchestration tools like Kubernetes.

- Contributions to open-source projects in the ML/Data Engineering space.

- Experience with Agile development methodologies.