Data Science Leader

1 day ago

Mumbai, Maharashtra, India beBeeMachineLearning Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Senior Machine Learning Engineer

We are seeking an experienced Senior Machine Learning Engineer to join 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.

Certifications:

  • 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.

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