Sr Machine Learning Engineer
Tasks
- Automate workflows and build reusable frameworks
- Build MLOps and LLMOps pipelines
- Build scalable ETL/ELT pipelines
- Define technical architecture and engineering standards
- Deploy monitor optimize machine learning and generative ai systems
- Design production machine learning applications
- Develop ingestion transformation migration and deployment pipelines
- Develop model evaluation and observability frameworks
- Implement data quality security privacy access control and logging
- Implement rag embeddings vector databases and agentic workflows
- Manage agile delivery with jira confluence and devops tools
- Orchestrate llm serving and knowledge layers
Perks/Benefits
- N/A
Skills/Tech-stack
AI Observability | AWS | Agentic Workflows | Amazon Bedrock | Apache Kafka | Apache Spark | CI/CD | Confluence | Databricks | Databricks SQL | Debezium | Docker | Dspy | Embeddings | Feature Store | Git | GitHub | Jira | Knowledge graphs | Kubernetes | LLM Evaluation | Langchain | Langgraph | Llamaindex | MLflow | Model Registry | OpenAI API | Prompt engineering | PySpark | Python | RAG | SQL | Vector Database
Education
Associate Degree | Bachelor of Engineering | Bachelor of Science | Master of Science | PhD
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