JOB SUMMARY:

We are expanding our team of motivated technologists to build AI and ML solutions for our customer. Specifically looking for an ML Engineer who is passionate about helping customers build Data Science and AI/ML solutions at scale. Your insight and expertise will help our delivery teams build ML solutions and build solutions across Data Science, Machine learning, Generative AI, databases, security, and automation. In addition, you will work with mid-tier technologies that include application integration, security, and much more!
This position is ideal for candidates with a strong foundation in machine learning principles, data processing, and software engineering. You will support the design, development, and deployment of ML models and pipelines, as well as assist in ingesting and transforming data for machine learning use cases.

REQUIRED QUALIFICATIONS:

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• Bachelor’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.

• Experience in machine learning, data engineering, or software development roles (internships or academic projects acceptable).
• Solid understanding of supervised learning, classification, and data preprocessing techniques.
• Experience with data engineering concepts, including SQL, PostgreSQL, and REST API integration
• Basic knowledge of data ingestion and transformation concepts.
• Proficiency in Python and common ML libraries (e.g., scikit-learn, pandas, NumPy, TensorFlow or PyTorch).
• Familiarity with full-stack or web-based ML applications (e.g., React, Django, or Android Studio projects).
• Familiarity with SQL and data wrangling tools.
• Experience with version control tools like Git.
• Strong problem-solving skills and attention to detail.
• Effective communication and documentation skills.
• Enthusiasm for learning new tools and growing within a collaborative team environment

PREFERRED QUALIFICATIONS:

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• Exposure to cloud platforms such as AWS, GCP, or Azure.

• Experience with pyton, Spark, Airflow, or data pipeline frameworks.
• Understanding of basic data architecture concepts (e.g., data lakes, warehouses).
• Participation in ML/DS projects, hackathons, or Kaggle competitions.

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• Assist in developing, training, and validating machine learning models for real-world applications (e.g., classification, prediction, and recommendation systems).

• Build and maintain data ingestion pipelines from structured and unstructured sources using Python and SQL-based tools
• Perform data cleaning, normalization, and feature engineering to prepare high-quality datasets for ML training and evaluation.
• Collaborate on ML projects such as outcome prediction systems, image classification models, and intelligent search interfaces.
• Contribute to building interactive applications by integrating ML models into frontend/backend systems (e.g., React, Django, REST APIs).
• Participate in MLOps workflows, including model versioning, basic deployment tasks, and experiment tracking.
• Document data flows, ML experiments, and application logic consistently.
• Attend Agile meetings and collaborate with peers through code reviews and sprint activities.