Machine Learning Engineer

You will design, develop, deploy, and maintain advanced machine learning models and data analysis systems to support specialized domain modeling and proprietary feature engineering. You will analyze structured and unstructured datasets, perform applied experimentation, develop production grade machine learning pipelines, optimize modeling infrastructure, and collaborate across business and engineering teams to translate business needs into scalable data driven solutions. You will document technical designs including model design specifications, architecture diagrams, data-flow documentation, and systems integration requirements to support maintainability and long term scalability. You will develop AI driven automation solutions using Large Language Models to streamline internal workflows, design LLM based agentic processes, validate automated outputs, and measure accuracy and efficiency improvements. You will design and develop internal software tools and backend services that support data quality, enable analytical workflows, expose model insights to internal teams, and integrate with organizational data systems and APIs. No travel is required.

Responsibilities

• Conduct applied research and experimentation to design, train, evaluate, and refine machine learning models, including performing feature engineering, selecting modeling techniques, validating model performance, and documenting analytical methods.
• Develop, test, and deploy production machine learning systems by managing the complete MLOps lifecycle, including experiment tracking, model versioning, containerization, orchestration of automated workflows, and monitoring of model performance in production environments.
• Maintain and optimize machine learning infrastructure to support training, inference, and data processing workflows, including configuring cloud compute environments, tuning distributed computation jobs, and improving system efficiency and scalability.
• Collaborate with business stakeholders to translate analytical requirements into quantifiable modeling objectives, define evaluation criteria, validate assumptions with data, and communicate analytical findings and modeling results.
• Design, prepare, and review technical documentation, including model design specifications, architecture diagrams, data-flow documentation, and systems integration requirements to support maintainability and long-term scalability.
• Develop AI-driven automation solutions using Large Language Models to streamline internal workflows, design LLM-based agentic processes, validate automated outputs, and measure accuracy and efficiency improvements.
• Design and develop internal software tools and backend services that support data quality, enable analytical workflows, expose model insights to internal teams, and integrate with organizational data systems and APIs.
• No travel is required.

Requirements

• 1 year of experience designing, training, and evaluating machine learning models using Python-based data science libraries, including feature engineering and developing predictive and descriptive models using tools such as scikit-learn and pandas.
• Experience using MLflow or similar platforms for experiment tracking, reproducible training workflows, and model versioning.
• Experience using Docker for containerization to package machine learning pipelines and ensure reproducible deployment environments.
• 1 year of experience orchestrating data processing and machine learning workflows, including scheduling, monitoring, and managing dependencies using Apache Airflow.
• 1 year of experience using CICD tools to automate model training, testing, and deployment processes.
• 1 year of experience configuring and optimizing cloud compute environments to support training, inference, and large-scale data processing tasks.
• 1 year of experience developing AI-driven automation workflows using language models, including integrating language-based model components into analytical or operational processes.
• Experience developing backend services and API using FastAPI, REST, or GraphQL to support data access, model serving, and analytical tooling.
• 1 year of experience working with SQL databases, including PostgreSQL, and cloud data platforms (e.g., Snowflake), including writing analytical queries and implementing data-quality validation workflows.
• Experience using distributed data-processing tools, including Spark, for large-scale data transformation and feature-engineering operations.