Data & Machine Learning Engineer
New
MexicoFull-TimeSenior
Salary not disclosed
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Job Details
- Languages
- English
- Experience
- Minimum of 8 years of experience in Data Engineering, with at least 2 years focused on MLOps or machine learning workflows.
- Required Skills
- AWSPythonSQLHadoopKafkaSnowflakeAzureSparkRedshiftMLOps
Requirements
- Minimum of 8 years of experience in Data Engineering, with at least 2 years focused on MLOps or machine learning workflows.
- Strong proficiency in Python for large-scale data processing, transformation, and engineering tasks.
- Solid experience designing and maintaining distributed data pipelines using tools such as Apache Spark, Hadoop, and Kafka.
- Deep understanding of SQL and relational database systems, including BI and data warehousing methodologies (e.g., Snowflake, Redshift).
- Hands-on experience with vector databases and RAG architectures for semantic search and LLM applications.
- Experience integrating LLM frameworks into production systems, including inference, fine-tuning, and orchestration.
- Familiarity with cloud platforms such as AWS or Azure, particularly for ML and data workloads.
- Strong understanding of software engineering principles, version control, CI/CD, and Agile development practices.
- Excellent communication skills in English, with the ability to collaborate across technical and business teams.
- Strong analytical mindset with a clear understanding of how data systems support business intelligence and decision-making.
Responsibilities
- Design, build, and maintain scalable data pipelines supporting ingestion, transformation, and delivery into data warehouses, feature stores, and ML/AI systems.
- Develop workflows for processing unstructured data and building semantic representations to enable advanced search, retrieval, and LLM-powered applications.
- Build and optimize analytics and BI solutions, including natural language querying and AI-driven insight generation.
- Implement and manage LLM-related workflows, including prompt engineering, orchestration pipelines, and model fine-tuning processes.
- Design and maintain vector database solutions and indexing strategies to support retrieval-augmented generation (RAG) systems.
- Collaborate with stakeholders to translate business and product requirements into scalable data and ML solutions.
- Ensure proper documentation of data pipelines, ML workflows, and deployment processes to maintain transparency and reproducibility.
- Stay up to date with emerging trends in data engineering, MLOps, and LLM technologies, contributing to continuous improvement.
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