Machine Learning Engineer, Specialist

We are seeking a Machine Learning Engineer to join our AI/ML Engineering team. You will be responsible for developing and optimizing complex data pipelines, integrating model pipelines, and building scalable AI/ML solutions, including large language models (LLMs). The ideal candidate will possess a robust background in traditional machine learning, deep learning, and significant experience with large datasets and cloud-based AI services.

Responsibilities:

• Develop and optimize complex data pipelines, applying machine learning engineering principles to enhance efficiency and scalability.
• Integrate and optimize data and model pipelines within production environments, diagnosing data inconsistencies and documenting assumptions.
• Employ experimental methodologies, statistics, and machine learning concepts to create self-running AI systems for predictive modeling.
• Collaborate with data science teams to review model-ready datasets and feature documentation, ensuring completeness and accuracy.
• Perform data discovery and analysis of raw data sources, applying business context to meet model development needs.
• Comfort with exploratory data exploration and tracking data lineage during inception or root cause analysis.
• Engage with internal stakeholders to understand business processes and translate requirements into analytical approaches.
• Write and maintain model monitoring scripts, diagnosing issues and coordinating resolutions based on alerts.
• Serve as a domain expert in machine learning engineering on cross-functional teams for significant initiatives.
• Stay updated with the latest advancements in AI/ML and apply them to real-world challenges.
• Participate in special projects and additional duties as assigned.

• Undergraduate degree or equivalent experience; a graduate degree is preferred.
• Minimum of 5 years of relevant work experience.
• At least 3 years of hands-on experience designing ETL pipelines using AWS services (e.g., Glue, SageMaker).
• Proficiency in programming languages, particularly Python (including PySpark, PySQL) and familiarity with machine learning libraries and frameworks.
• Strong understanding of cloud technologies, including AWS and Azure, and experience with NoSQL databases.
• Familiarity with Feature Store usage, LLMs, GenAI, RAG, Prompt Engineering, and Model Evaluation.
• Experience with API design and development is a plus.
• Solid understanding of software engineering principles, including design patterns, testing, security, and version control.
• Knowledge of Machine Learning Development Lifecycle (MDLC) best practices and protocols.
• Understanding of solution architecture for building end-to-end machine learning data pipelines.