
Senior Machine Learning Specialist
15 hours ago
We are seeking an experienced Machine Learning Engineer to lead our AI/ML Engineering team.
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.
Key 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.
Requirements:
- Undergraduate degree or equivalent experience; a graduate degree is preferred.
- Minimum of 8 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. Docker Kubernetes
- 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.
Benefits:
This role offers the opportunity to work on cutting-edge AI projects, collaborate with experienced engineers, and develop expertise in machine learning engineering.
What We Offer:
A competitive salary and benefits package, opportunities for professional growth, and a dynamic work environment.
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