Machine Learning Engineer

Data Capital Incorporation is a company focused on leveraging data for impactful solutions, and they are seeking a Machine Learning Engineer to enhance their ML capabilities. The role involves developing and productionizing machine learning models, integrating them with business systems, and utilizing tools such as AWS and Databricks for data management and model deployment.

Responsibilities:

• 5+ years of experience in ML Engineering or Applied Machine Learning
• Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow)
• Proficient with Databricks, MLflow, and PySpark
• Solid understanding of model lifecycle and MLOps practices
• Experience with AWS-based data infrastructure and related DevOps practices
• Demonstrated ability to productionize models and integrate with business system
• Strong understanding of mathematics and statistics relevant to machine learning and AI
• Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.)
• Solid background in software engineering principles and best practices
• Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face)
• Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.)
• Practical experience with LLMs, RAGs, and AI agent architectures
• Proficiency with the Databricks platform for data engineering and ML pipelines
• Advanced programming skills in Python
• Excellent communication and teamwork abilities

Requirements:

• 5+ years of experience in ML Engineering or Applied Machine Learning
• Strong Python skills and hands-on experience with ML libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow)
• Proficient with Databricks, MLflow, and PySpark
• Solid understanding of model lifecycle and MLOps practices
• Experience with AWS-based data infrastructure and related DevOps practices
• Demonstrated ability to productionize models and integrate with business systems
• Strong understanding of mathematics and statistics relevant to machine learning and AI
• Proven experience with machine learning models and algorithms (supervised, unsupervised, deep learning, etc.)
• Solid background in software engineering principles and best practices
• Hands-on experience with model training frameworks (e.g., TensorFlow, PyTorch, Hugging Face)
• Experience with MLOps tools and workflows, particularly on AWS (SageMaker, Lambda, S3, etc.)
• Practical experience with LLMs, RAGs, and AI agent architectures
• Proficiency with the Databricks platform for data engineering and ML pipelines
• Advanced programming skills in Python
• Excellent communication and teamwork abilities