Machine Learning Engineer
Tasks
- Architect distributed systems
- Build ETL processes
- Build fine tuning pipelines
- Create prompt engineering strategies
- Design scalable data pipelines
- Develop LLM evaluation frameworks
- Develop batch data workflows
- Implement PII detection and redaction
- Implement data quality frameworks
- Integrate LLMs into production applications
- Monitor data integrity
- Optimize data processing jobs
- Troubleshoot production data processing issues
Perks/Benefits
- N/A
Skills/Tech-stack
API Integration | Algorithms | Amazon S3 | Apache Spark | Azure Data | Azure Data Lake | Azure OpenAI | Best practices | CI/CD | Consistency models | Data Governance | Data Lake | Data Partitioning | Data Pipeline Architecture | Data Structures | Data pipeline | Design Patterns | Distributed Computing | Distributed Consensus | Docker | Docker containers | ETL | FastAPI | Fault Tolerance | Fine Tuning | Flask | Git | HDFS | Kubernetes | Langchain | Language Models | Large Language Models | Llamaindex | Load Balancing | MLOps | Machine Learning | Machine Learning Pipelines | OpenAI | PII Detection | Pipeline architecture | Prompt engineering | Python | REST APIs | Ray | Replication | Resource Management | SQL | Security best practices | Sharding strategies | Software Design | Software Design Patterns
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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