Senior Machine Learning Engineer
Washington, District of Columbia, USA
USD 120K-175K (estimate) Senior-level Full Time
Tasks
- Architect ML AI platform
- Build RAG systems with vector databases
- Build data pipelines for batch and streaming
- Deploy ML models into production
- Design feature stores and model serving
- Develop evaluation monitoring and safety frameworks
- Ensure data quality lineage reliability
- Implement CI/CD for ML systems
- Implement Data Security & Compliance
- Improve system reliability latency and cost efficiency
- Manage model lifecycle training versioning deployment monitoring
- Mentor engineers and raise ML team standards
- Optimize LLM latency cost and quality
- Orchestrate multi step agent workflows with tool use memory retrieval
- Set MLOps and LLMOps best practices
Perks/Benefits
Skills/Tech-stack
AWS | Agent systems | Agentic Workflows | Airflow | Azure | CI/CD | DBT | Data Lineage | Data Quality | Data Streaming | Distributed Systems | Evaluation Frameworks | Experiment tracking | Feature Store | GCP | HIPAA | LLM orchestration | LLMOps | MLOps | Machine Learning | Model Monitoring | Model Serving | Model versioning | Multi-Agent | Multi-Agent Systems | Observability | Prompt engineering | Prompt management | RAG | Reinforcement Learning | SOC2 | Spark | Vector Databases
Education
N/A
Regions
Countries
States
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